Category: 3. Business

• Group sales grew by 7%¹ at constant exchange rates (CER; 2% in CHF), driven by strong demand for medicines and diagnostic solutions.
• Sales in the fourth quarter increased by 8%, reflecting the positive momentum.
• Pharmaceuticals Division sales increased by 9% (3% in CHF), with Phesgo (breast cancer), Xolair (food allergies), Ocrevus (multiple sclerosis), Hemlibra (haemophilia A) and Vabysmo (severe eye diseases) being the top growth drivers.
• Diagnostics Division sales grew 2% (-3% in CHF) as demand for pathology and molecular solutions continued to more than offset the impact of healthcare pricing reforms in China.
• Core operating profit increased by 13% (5% in CHF), driven by higher sales and efficiency gains.
• Core earnings per share showed growth of 11% (4% in CHF); IFRS net income increased by 58% (50% in CHF), due to the strong operational performance in 2025 and the base effect of impairment charges in 2024.
• Highlights:
  • US and EU approval for the subcutaneous form of Lunsumio for a type of blood cancer
  • EU approval for Gazyva/Gazyvaro for lupus nephritis, a serious kidney disease
  • Positive data on several therapies: (phase III) giredestrant for breast cancer, fenebrutinib for two forms of multiple sclerosis, Gazyva/Gazyvaro for two immune-related diseases, PiaSky for a rare, life-threatening kidney condition and Enspryng for a rare autoimmune disease that affects the brain, spinal cord and optic nerves; (phase II) CT-388 for obesity
  • Advancement of 10 key molecules into phase III development in 2025
  • EU CE mark for novel Elecsys Dengue Ag test to diagnose dengue and for cobas BV/CV assay to improve diagnostic accuracy for women affected by vaginitis
  • CE Mark for test to monitor antibiotic therapies, expanding the only automated mass spectrometry platform on the market to an in vitro diagnostic menu of 39 tests
• Board proposes a dividend increase to CHF 9.80 per share and non-voting equity security. If approved by shareholders, this would be the 39th consecutive dividend increase.
• Change in Board of Directors

Outlook for 2026

Roche (SIX: RO, ROG; OTCQX: RHHBY) expects an increase in Group sales in the mid single digit range (CER) for 2026. Core earnings per share are targeted to develop in the high single digit range (CER). Roche expects to further increase its dividend in Swiss francs. 

Key figures	CHF millions		% change
January–December	2025	2024	At CER1	In CHF
Group sales	61,516	60,495	7	2
Pharmaceuticals Division	47,669	46,171	9	3
Diagnostics Division	13,847	14,324	2	-3
Core operating profit	21,833	20,823	13	5
Core EPS – diluted (CHF)	19.46	18.80	11	4
IFRS net income	13,799	9,187	58	50

Roche CEO Thomas Schinecker: “2025 was a strong year for Roche, reflecting our continued focus on operational and R&D excellence.

We have significant momentum across our pharmaceutical pipeline: ten potential new medicines advanced into final-stage development, and 12 late-stage clinical studies delivered positive results. We had important breakthroughs in lupus and oestrogen receptor-positive breast cancer, which accounts for approximately 70% of all breast cancer cases, as well as the first positive late-stage clinical results in a new therapy for multiple sclerosis.

We are also setting new standards in diagnostics: our next-generation sequencing technology, which will be launched this year, decoded an entire human genome in less than four hours.

With our strong financial performance and our continued progress in innovation, we are well positioned for growth.”

Change in Board of Directors

The Board of Directors will propose Lubomira Rochet (1977), Executive Vice President and member of the Group Executive Committee of Societe Generale, for election as a new Board member at the upcoming Annual General Meeting. Severin Schwan, Chairman of the Board: “Lubomira Rochet brings a broad leadership track record and deep experience in business transformations through digital and technology. I am very pleased that we can propose her for election to the Board of Directors.”

As previously announced, Dr Claudia Suessmuth Dyckerhoff has decided not to stand for re-election as a member of the Roche Board of Directors at the Annual General Meeting in 2026.

Group results

In 2025, Roche achieved sales growth of 7% (2% in CHF) to CHF 61.5 billion due to strong demand for pharmaceutical products and diagnostic solutions.

The appreciation of the Swiss franc against most currencies, notably the US dollar, had a significant impact on the results reported in Swiss francs compared to constant exchange rates.

Core operating profit increased by 13% (5% in CHF) to CHF 21.8 billion, driven by higher sales and efficiency gains.

Core earnings per share increased by 11% (4% in CHF).

IFRS net income increased by 58% (50% in CHF) to CHF 13.8 billion due to the strong operating performance in 2025 and the base effect of impairment charges in 2024.

Sales in the Pharmaceuticals Division increased by 9% (3% in CHF) to CHF 47.7 billion, with medicines for severe diseases continuing their strong growth.

The top five growth drivers – Phesgo, Xolair, Ocrevus, Hemlibra and Vabysmo – achieved total sales of CHF 21.4 billion, an increase of CHF 3.2 billion (CER) compared to 2024.

Sales of products with expired patents – Avastin (various types of cancer), Herceptin (breast and gastric cancer), MabThera/Rituxan (blood cancer, rheumatoid arthritis), Esbriet (lung disease), Lucentis (severe eye diseases) and Actemra/RoActemra (rheumatoid arthritis) – decreased by a combined CHF 0.7 billion (CER).

In the United States, sales rose by 8% due to continued growth of Xolair and continuing uptake of Ocrevus, Phesgo, Hemlibra and Polivy (blood cancer). This growth more than compensated for the decline in sales of medicines with expired patents.

Sales in Europe grew 5% as strong demand for Ocrevus and Vabysmo and the continuing uptake of Polivy, Hemlibra and Phesgo more than compensated for the lower sales of Perjeta (breast cancer) due to the ongoing conversion of patients to Phesgo, and the impact of biosimilar competition on Actemra/RoActemra sales.

In Japan, sales increased by 5%, mainly due to the strong uptake of Phesgo, Vabysmo, Hemlibra, Enspryng (acute inflammation of optic nerve and spinal cord) and PiaSky (paroxysmal nocturnal haemoglobinuria). Sales growth was partially offset by the decline in sales of Avastin because of biosimilar erosion and Perjeta due to the continued conversion of patients to Phesgo.

Sales in the International region rose by 14%, led by Phesgo, Xofluza (influenza), Hemlibra, Vabysmo, Elevidys (Duchenne muscular dystrophy) and Polivy. In China, sales rose by 10%, driven by the uptake of Phesgo due to the inclusion in the government drug reimbursement list, strong sales of Xofluza and the continued roll-out of Vabysmo and Polivy.

The Diagnostics Division’s sales increased by 2% (-3% in CHF) to CHF 13.8 billion as growth in demand for pathology and molecular solutions more than offset the impact of healthcare pricing reforms in China.

Sales in the Europe, Middle East and Africa (EMEA) region increased by 6%, driven by higher sales of clinical chemistry and immunodiagnostic products. In North America, sales increased by 9%, with growth across all customer areas. Sales in Asia-Pacific decreased by 12% due to healthcare pricing reforms in China. In Latin America, sales grew by 11%.
 

Pharmaceuticals Division: pipeline

With 66 new molecular entities (NMEs) and a total of 107 projects, Roche has a promising pipeline with a wide variety of therapeutic approaches.

Pharmaceuticals research and development (R&D) expenditure decreased by 3% to CHF 10.4 billion (Group R&D: -3% to CHF 12.2 billion). Oncology remained the primary area for R&D, with substantial investments also in the areas of cardiovascular, renal and metabolism and immunology.

Pharmaceuticals: key developments

Compound	Milestone
Regulatory
Lunsumio Blood cancer	FDA approves Lunsumio VELO for subcutaneous use in relapsed or refractory follicular lymphoma Lunsumio VELO reduces administration time from 2‒4 hours to approximately 1 minute. Availability of Lunsumio VELO allows treatment aligned to people’s clinical needs and personal preferences. The approval is supported by data demonstrating compelling complete response rate in third-line or later treatment of people with follicular lymphoma, a disease that typically becomes harder to treat after each relapse.
Gazyva/Gazyvaro Lupus nephritis	European Commission approves Gazyva/Gazyvaro for adults with active lupus nephritis Approval based on phase II NOBILITY and phase III REGENCY studies showing superiority of Gazyva/Gazyvaro over standard therapy alone. Gazyva/Gazyvaro is the only anti-CD20 antibody to demonstrate a benefit in a complete renal response in lupus nephritis in a randomised phase III study. Gazyva/Gazyvaro could become a new standard of care for up to an estimated 135,000 people affected by lupus nephritis in the European Union, potentially helping to delay or prevent end-stage kidney disease.
Lunsumio Blood cancer	European Commission approves Lunsumio subcutaneous for relapsed or refractory follicular lymphoma Lunsumio provides high rates of deep and long-lasting responses in third-line and later treatment of people with follicular lymphoma, a disease that typically becomes harder to treat each time a patient relapses. Lunsumio subcutaneous offers a new treatment option that can significantly reduce administration time to approximately 1 minute. Lunsumio SC allows patients to receive treatment aligned to clinical requirements and lifestyle preferences.
Phase III, pivotal and other key read-outs
CT-388 Obesity	Roche announces positive phase II results for its dual GLP-1/GIP receptor agonist CT-388 in people living with obesity A once-weekly subcutaneous injection of CT-388 achieved a statistically significant placebo-adjusted weight loss of 22.5% (p < 0.001) at 48 weeks at the highest dose tested (24 mg), without reaching a weight loss plateau. 54% of participants on the 24 mg dose achieved resolution of obesity (BMI <30 kg/m2) vs 13% in the placebo group. CT-388 demonstrated a safety and tolerability profile generally consistent with that of its drug class with no new or unexpected safety signals.
Giredestrant Breast cancer	Giredestrant reduces risk of invasive disease recurrence or death by 30% in ER-positive early-stage breast cancer Giredestrant is the only oral SERD to show superior invasive disease-free survival in the adjuvant setting, marking the first significant endocrine therapy advance in over 20 years1‒3. Transformational results support the potential of giredestrant to become a new standard-of-care for early-stage disease. ER-positive breast cancer accounts for approximately 70% of breast cancer cases, and up to a third of patients experience recurrence on or after adjuvant endocrine therapy treatment.
Lunsumio Blood cancer	Roche presents Lunsumio data showing potential across earlier treatment lines in indolent and aggressive lymphomas Lunsumio in combination with lenalidomide may offer an effective treatment in relapsed or refractory follicular lymphoma based on first data from single-arm US cohort of phase III CELESTIMO study. Data from subcutaneous Lunsumio plus Polivy reinforce its outpatient, chemotherapy-free potential in people with relapsed or refractory large B-cell lymphoma. Results highlight the potential of innovative Lunsumio combination regimens to offer improved outcomes for more people with lymphoma earlier in their disease.
Columvi Blood cancer	Columvi combination shows sustained survival benefit at three-year follow-up of pivotal phase III STARGLO study Overall survival was twice as long for people treated with Columvi in combination with GemOx versus MabThera/Rituxan plus GemOx. This Columvi combination is available off-the-shelf and could offer a potentially curative treatment option for people with relapsed or refractory diffuse large B-cell lymphoma (R/R DLBCL) who are not candidates for transplant. Columvi in combination with GemOx has now been approved in more than 50 countries worldwide and is recommended in international treatment guidelines.
Giredestrant Breast cancer	Giredestrant becomes the first oral SERD to show superior invasive disease-free survival in early breast cancer At interim analysis, giredestrant demonstrated a statistically significant and clinically meaningful benefit versus standard-of-care endocrine monotherapy. These unprecedented results support its potential as a new standard-of-care endocrine therapy in the early-stage setting. lidERA is the second positive phase III read-out for giredestrant following evERA presented at ESMO 2025.
Fenebrutinib Multiple sclerosis	Fenebrutinib shows unprecedented positive phase III results as the potential first and only BTK inhibitor in both relapsing multiple sclerosis (RMS) and primary progressive multiple sclerosis (PPMS) The first pivotal RMS study (FENhance 2) met its primary endpoint, with fenebrutinib significantly reducing relapses compared to teriflunomide. In the pivotal PPMS study (FENtrepid), fenebrutinib slowed disability progression at least as effectively as Ocrevus, the only approved therapy in PPMS. Full data from both studies will be shared at upcoming medical meetings; all data will be considered for regulatory submission after the second RMS study (FENhance 1) reads out, expected in the first half of 2026.
Haematology	Roche presents new data from its broad and innovative haematology portfolio at ASH 2025 Findings demonstrate the effectiveness of Roche’s approved medicines in advancing treatment standards for people with blood disorders. Data from innovative pipeline signals progress towards improved outcomes in haemophilia A, lymphoma and multiple myeloma.
Gazyva/Gazyvaro Systemic lupus erythematosus	Positive phase III data for Gazyva/Gazyvaro show significant reduction in disease activity for systemic lupus erythematosus (SLE) Phase III ALLEGORY study met primary and all key secondary endpoints with Gazyva/Gazyvaro, demonstrating significant reduction in disease activity for SLE. Gazyva/Gazyvaro has the potential to become a transformative new standard of care for up to 3.4 million people affected by SLE worldwide, and would be the first anti-CD20 therapy for SLE to directly target B cells if approved. These positive results follow recent US FDA approval and positive EU CHMP opinion for Gazyva/Gazyvaro in lupus nephritis, as well as positive phase III data from the INShore study in idiopathic nephrotic syndrome.
Gazyva/Gazyvaro Idiopathic nephrotic syndrome	Positive phase III results for Gazyva/Gazyvaro in children and young adults with idiopathic nephrotic syndrome Gazyva/Gazyvaro versus mycophenolate mofetil shows significantly more children and young adults achieved sustained complete remission at week 52. If approved, Gazyva/Gazyvaro could help children and young adults sustain remission, potentially with a reduced need for steroids to manage their disease. INShore is the first global phase III study of a targeted therapy in this chronic kidney disease commonly diagnosed in early childhood.
Other
89bio tender offer	Roche completes tender offer for 89bio, Inc. shares and prepares to finalise acquisition Roche’s subsidiary Bluefin Merger Subsidiary, Inc. accepted for payment all shares validly tendered and not withdrawn in its tender offer for 89bio at USD 14.50 per share in cash plus a contingent value right for up to USD 6.00 per share. Approximately 94,113,710 shares, representing about 60.49% of 89bio’s outstanding common stock, were validly tendered and not withdrawn in the offer, and the tender offer expired on 29 October 2025. Roche intends to complete the acquisition of 89bio through a merger, after which 89bio will become a wholly owned subsidiary of Roche and its shares will be delisted from Nasdaq.

  

Pharmaceuticals sales

Sales	CHF millions		As % of sales		% change
January–December	2025	2024	2025	2024	At CER	In CHF
Pharmaceuticals Division	47,669	46,171	100.0	100.0	9	3
United States	25,355	24,774	53.2	53.7	8	2
Europe	9,164	8,832	19.2	19.1	5	4
Japan	2,882	2,874	6.0	6.2	5	0
International	10,268	9,691	21.6	21.0	14	6

International: Asia-Pacific, CEETRIS (Central Eastern Europe, Türkiye, Russia and Indian subcontinent), Latin America, Middle East, Africa, Canada, others                                

Top 20 best-selling pharmaceuticals	Total		United States		Europe		Japan		International
	CHF m	%	CHF m	%	CHF m	%	CHF m	%	CHF m	%
Ocrevus Multiple sclerosis	7,010	9	4,874	7	1,451	13	–	–	685	21
Hemlibra Haemophilia A	4,754	11	2,665	6	1,002	10	377	8	710	38
Vabysmo Eye diseases (nAMD, DME, RVO)	4,102	12	2,857	3	741	21	146	22	358	116
Tecentriq Cancer immunotherapy	3,566	3	1,640	-2	878	3	349	-4	699	18
Xolair2 Asthma, food allergies	3,075	32	3,075	32	–	–	–	–	–	–
Perjeta2 Breast cancer	2,968	-13	1,268	0	552	-13	69	-37	1,079	-21
Actemra/RoActemra2 RA, COVID-19	2,470	-2	1,206	-4	588	-9	310	5	366	16
Phesgo Breast cancer	2,441	48	708	31	812	12	188	44	733	172
Kadcyla2 Breast cancer	2,025	7	768	6	532	-4	91	-3	634	22
Evrysdi Spinal muscular atrophy	1,757	13	612	10	616	9	90	1	439	25
Alecensa Lung cancer	1,562	6	565	14	262	-6	204	7	531	5
Polivy Blood cancer	1,470	38	688	28	290	53	207	9	285	87
MabThera/Rituxan2 Blood cancer, RA	1,251	-4	794	0	140	-5	14	-11	303	-12
Activase/TNKase2 Cardiac diseases	1,107	-2	1,056	-2	–	–	–	–	51	-11
Herceptin2 Breast and gastric cancer	1,028	-22	225	-10	291	-2	7	-43	505	-32
Gazyva/Gazyvaro2 Blood cancer	986	14	519	19	245	2	35	25	187	15
Avastin2 Various cancer types	973	-17	299	-17	70	-16	145	-23	459	-14
Pulmozyme2 Cystic fibrosis	479	12	343	20	65	-9	1	-12	70	1
Xofluza Influenza	407	184	57	66	2	*	–	–	348	219
CellCept2 Immunosuppressant	385	1	21	-5	131	8	44	17	189	-6

* Over 500%
DME: diabetic macular edema / nAMD: neovascular or ‘wet’ age-related macular degeneration / RVO: retinal vein occlusion / RA: rheumatoid arthritis

Diagnostics Division: portfolio

In Diagnostics, Roche introduced two instrument platforms, six digital solutions and 53 new tests in 2025.

The main areas of R&D activity included the development of high medical value assays, notably for the oncology disease area, as well as of digital solutions and sequencing. In addition, there were continuing investments in cardiometabolic diseases, particularly for continuous blood glucose monitoring.

Diagnostics: key developments

Product	Milestone
cobas Mass Spec solution	Roche expands automated mass spectrometry menu with antibiotics drug monitoring CE mark approval offering industry’s broadest in vitro diagnostic menu With this approval, Roche’s automated mass spectrometry platform now offers the industry’s broadest in vitro diagnostic menu with 39 tests, including tests for therapeutic drug monitoring for immunosuppressants and antibiotics, as well as steroid hormones and vitamin D metabolites. The comprehensive menu brings the sensitivity and specificity of gold-standard testing into routine labs for a wide range of the most frequently tested targets. The fully automated solution replaces labour-intensive manual workflows, reducing turnaround times and supporting faster, standardised, high-quality care.
cobas BV/CV test Vaginitis	Roche launches new PCR test to help improve diagnostic accuracy for women affected by vaginitis in countries following the CE mark The new PCR test aids in the diagnosis of infectious causes of vaginitis through the detection of bacteria associated with bacterial vaginosis and yeast associated with candida vaginitis. The test will help improve diagnostic accuracy for millions of women affected by vaginitis annually, delivering more accurate and specific results. This test offers faster diagnosis by using a single vaginal swab for broader sexual health testing, eliminating the need for an additional sample.
cobas liat Bordetella panel Infectious diseases	Roche receives FDA clearance with CLIA waiver and CE mark for its first point-of-care test for diagnosing Bordetella infections, including whooping cough (pertussis) The point-of-care test delivers PCR-accurate results in just 15 minutes, enabling healthcare providers to act quickly and prevent severe complications and onward transmission. The test detects and differentiates between three types of Bordetella infection that can cause similar cough symptoms, ensuring patients receive the right diagnosis at the earliest opportunity. Early diagnosis can reduce the risk of complications and severe disease in vulnerable groups such as infants and the elderly, by enabling faster, more precise care decisions.
Elecsys Dengue Ag test Dengue	Roche receives CE mark for novel automated high-throughput Elecsys Dengue Ag test to diagnose dengue New dengue antigen test delivers high clinical sensitivity and specificity, as well as inclusivity for all four dengue virus serotypes, helping clinicians confidently distinguish dengue from other acute fever-causing illnesses. Full automation facilitates medium to high throughput and enables improvement of lab efficiency and test traceability, while reducing the risk of human error. Test delivers results in just 18 minutes, enabling faster laboratory workflows and patient management during outbreaks.

Diagnostics sales

Sales	CHF millions		As % of sales		% change
January–December	2025	2024	2025	2024	At CER	In CHF
Diagnostics Division	13,847	14,324	100.0	100.0	2	-3
Customer areas3
Core Lab	7,614	8,011	55.0	55.9	0	-5
Molecular Lab	2,527	2,554	18.3	17.8	4	-1
Near Patient Care	1,983	2,160	14.3	15.1	-3	-8
Pathology Lab	1,723	1,599	12.4	11.2	14	8
Regions
Europe, Middle East, Africa	4,965	4,822	35.9	33.7	6	3
North America	4,444	4,335	32.1	30.3	9	3
Asia-Pacific	3,386	4,099	24.4	28.6	-12	-17
Latin America	1,052	1,068	7.6	7.4	11	-1

Roche’s Full Year Results 2025 – Live Webinar
There will be a live webinar for investors and analysts today, Thursday, 29 January at 1:30 pm CET. To access the webinar, please click here.

Additional information
Full-Year 2025 Presentation
Full-Year 2025 Presentation with appendix
Annual Report 2025
Finance Report
Pharmaceuticals: key product launches in 2025
Diagnostics: key product launches in 2025

About Roche
Founded in 1896 in Basel, Switzerland, as one of the first industrial manufacturers of branded medicines, Roche has grown into the world’s largest biotechnology company and the global leader in in-vitro diagnostics. The company pursues scientific excellence to discover and develop medicines and diagnostics for improving and saving the lives of people around the world. We are a pioneer in personalised healthcare and want to further transform how healthcare is delivered to have an even greater impact. To provide the best care for each person we partner with many stakeholders and combine our strengths in Diagnostics and Pharma with data insights from the clinical practice.

For over 125 years, sustainability has been an integral part of Roche’s business. As a science-driven company, our greatest contribution to society is developing innovative medicines and diagnostics that help people live healthier lives. Roche is committed to the Science Based Targets initiative and the Sustainable Markets Initiative to achieve net zero by 2045.

Genentech, in the United States, is a wholly owned member of the Roche Group. Roche is the majority shareholder in Chugai Pharmaceutical, Japan.

For more information, please visit www.roche.com.

All trademarks used or mentioned in this release are protected by law.

References
[1] Unless otherwise stated, all growth rates and year-on-year comparisons are at constant exchange rates (CER; average rates 2024) and all total figures quoted are reported in Swiss francs.
[2] Products launched before 2015.
[3] Core Lab: diagnostics solutions in the areas of immunoassays, clinical chemistry and CustomBiotech.
Molecular Lab: diagnostics solutions for pathogen detection and monitoring, donor screening, sexual health and genomics, genomic tumour profiling.
Near Patient Care: diagnostics solutions in emergency rooms, medical practices and directly with patients, including integrated personalised diabetes management.
Pathology Lab: diagnostics solutions for tissue biopsies and companion diagnostics.
In 2025, sales in the Pathology Lab customer area include sales previously reported in the Molecular Lab customer area to foster business transparency and harmonisation in the use of solutions in the area of cervical intraepithelial neoplasia technology (CINtec). The comparative information for 2024 has been restated accordingly.
In 2025, sales in the Core Lab customer area include sales previously reported in the Near Patient Care customer area to centralise digital healthcare solutions within Roche Information Solutions. The comparative information for 2024 has been restated accordingly.

Cautionary statement regarding forward-looking statements
This document contains certain forward-looking statements. These forward-looking statements may be identified by words such as ‘believes’, ‘expects’, ‘anticipates’, ‘projects’, ‘intends’, ‘should’, ‘seeks’, ‘estimates’, ‘future’ or similar expressions or by discussion of, among other things, strategy, goals, plans or intentions. Various factors may cause actual results to differ materially in the future from those reflected in forward-looking statements contained in this document, such as: (1) pricing and product initiatives of competitors; (2) legislative and regulatory developments and economic conditions; (3) delay or inability in obtaining regulatory approvals or bringing products to market; (4) fluctuations in currency exchange rates and general financial market conditions; (5) uncertainties in the discovery, development or marketing of new products or new uses of existing products, including without limitation negative results of clinical trials or research projects, unexpected side effects of pipeline or marketed products; (6) increased government pricing pressures; (7) interruptions in production; (8) loss of or inability to obtain adequate protection for intellectual property rights; (9) litigation; (10) loss of key executives or other employees; and (11) adverse publicity and news coverage. The statement regarding earnings per share growth is not a profit forecast and should not be interpreted to mean that Roche’s earnings or earnings per share for this or any subsequent period will necessarily match or exceed the historical published earnings or earnings per share of Roche.

Ad hoc announcement pursuant to article 53 LR

• Sales of CHF 7.5 billion, an increase of 5.1% on a like-for-like ¹ basis and an increase of 0.8% in Swiss francs
• Strong performance across all markets, segments and customer groups – high growth markets growing at 8.0% and mature markets at 2.4% on a like-for-like basis  
• EBITDA ² of CHF 1,751 million, an increase of 4.5% over 2024, when measured in local currency – EBITDA margin of 23.4%
• Comparable EBITDA ³ margin of 24.2% compared to 24.5% in 2024
• Net income of CHF 1,071 million; net profit margin of 14.3% of sales
• Free cash flow ⁴ of CHF 1,053 million, or 14.1% of sales
• Proposed dividend of CHF 72.00 per share, up 2.9% year-on-year
• Givaudan delivers on its ambitious targets of its 2025 strategy. With an average like-for-like sales growth of 6.8% and an average free cash flow of 12.5% for the period 2021–2025, the Company exceeded its targets  
• Changes to the Executive Committee and Board of Directors announced in a separate communication today

 

“We are very pleased with our strong financial performance in 2025, which has been achieved against very strong prior year comparables and in a volatile external environment. Furthermore we are very proud of our results over the five-year strategic planning period 2021–2025 where we have exceeded all of our financial ambitions. These industry leading results are a strong testament to the unique position of Givaudan in supporting the growth of our customers across our business and to our Givaudan colleagues for their passion and dedication in consistently delivering excellent results.”

Gilles Andrier, CEO

Sales performance

Full year Group sales were CHF 7,472 million, an increase of 5.1% on a like-for-like (LFL) basis and an increase of 0.8% in Swiss francs, compared to strong comparable growth of 12.3% LFL in 2024.

Givaudan finished the year positively, with good volume growth and the Company maintained its operations and global supply chain at a high level.

With higher input costs in 2025, including tariffs, the Company continued to implement price increases in collaboration with its customers to fully compensate for the increases in input costs.

Fragrance & Beauty sales were CHF 3,830 million, an increase of 7.9% LFL and 4.6% in Swiss francs.

On a business unit basis, Fine Fragrance sales increased by 18.3% LFL against a high prior year comparable growth of 18.4%, and Consumer Products sales increased by 6.8% LFL against a strong prior year comparable growth of 13.5%. Sales of Fragrance Ingredients and Active Beauty decreased by –1.4% LFL, with double-digit growth in Active Beauty offset by weaker performance in Fragrance Ingredients.

Sales in Taste & Wellbeing were CHF 3,642 million, an increase of 2.4% on a LFL basis and a decrease of –2.9% in Swiss francs, against a strong prior year comparable growth of 10.7% LFL.

On a regional basis, sales increased in South Asia, Middle East and Africa by 7.8% LFL, in Europe by 2.6% LFL, in North America by 3.0% LFL and in Latin America by 0.7% LFL. Sales decreased in Asia Pacific by –0.8% LFL. Within the product segments, there was broad based good growth in snacks, health care, dairy, and in sweet goods.

Gross margin

The gross profit decreased from CHF 3,271 million in 2024 to CHF 3,252 million in 2025, with the decrease mainly caused by foreign exchange effects. Measured in local currency, gross profit increased by 4.0%. With higher input costs in 2025, including tariffs, the gross margin decreased to 43.5% in 2025 compared to 44.1% in 2024.

Earnings Before Interest, Tax, Depreciation and Amortisation (EBITDA) ²

The EBITDA ² was CHF 1,751 million in 2025 compared to CHF 1,765 million in 2024. The EBITDA margin was 23.4% in 2025 compared to 23.8% in 2024, whilst on a comparable basis ³, the EBITDA margin decreased to 24.2% in 2025 compared to 24.5% in 2024.

The EBITDA of Fragrance & Beauty was CHF 985 million in 2025, flat compared to CHF 985 million in 2024. However, when measured in local currency, the EBITDA of Fragrance & Beauty increased by 4.2%. The EBITDA margin decreased to 25.7% in 2025 from 26.9% in 2024. On a comparable basis the EBITDA margin of Fragrance & Beauty was 26.5% in 2025 compared to 27.8% in 2024.

The EBITDA of Taste & Wellbeing was CHF 766 million in 2025 compared to CHF 780 million in 2024, a decrease of –1.8%. However, when measured in local currency, the EBITDA of Taste & Wellbeing increased by 4.8%. The EBITDA margin increased to 21.0% in 2025, from 20.8% in 2024. On a comparable basis the EBITDA margin of Taste & Wellbeing was 21.7% in 2025 compared to 21.3% in 2024.

Operating income

The operating income was CHF 1,381 million in 2025 compared to CHF 1,394 million in 2024, a decrease of –0.9%, mainly caused by the impact of foreign exchange rates. However, when measured in local currency terms, the operating income increased by 4.9%. The operating margin was 18.5% in 2025 compared to 18.8% in 2024.

The operating income for Fragrance & Beauty decreased from CHF 828 million in 2024 to CHF 819 million in 2025. The operating margin was 21.4% in 2025 compared to 22.6% in 2024.

In Taste & Wellbeing, the operating income was flat, CHF 562 million in 2025 compared to CHF 566 million in 2024. The operating margin increased to 15.4% in 2025 compared to 15.1% in 2024.

Financial performance

Financing costs in 2025 were CHF 113 million versus CHF 121 million in 2024. Other financial income, net of expenses, was CHF 37 million in 2025 compared with CHF 40 million of other financial income, net of expense in 2024.

The income tax expense as a percentage of income before taxes was 18%, compared to 17% in 2024.

Net income

The net income was CHF 1,071 million in 2025 compared to CHF 1,090 million in 2024, a decrease of –1.7% in Swiss francs. However, when measured in local currency, the net income increased by 3.9%. Net profit margin was 14.3% versus 14.7% in 2024. Basic earnings per share were CHF 116.08 compared to CHF 118.17 for the same period in 2024.

Cash flow

Givaudan delivered an operating cash flow of CHF 1,512 million in 2025, compared to CHF 1,625 million in 2024.

Net working capital as a percentage of sales was 22.0%, compared to 23.4% in 2024, with the Group having a continuing focus on the effective management of all aspects of working capital.

Total net investments in property, plant and equipment were CHF 233 million, compared to CHF 223 million in 2024.

Intangible asset additions were CHF 52 million in 2025, compared to CHF 57 million in 2024 as the Company continued to invest in its digital roadmap and in bringing all acquired entities on to the Givaudan operating platform.

Total net investments in tangible and intangible assets were 3.8% of sales in 2025, flat compared to 3.8% in 2024.

Operating cash flow after net investments was CHF 1,227 million in 2025, versus CHF 1,345 million in 2024. Free cash flow ⁴ was CHF 1,053 million in 2025, versus CHF 1,158 million for the comparable period in 2024. As a percentage of sales, free cash flow in 2025 was 14.1%, compared to 15.6% in 2024.

Financial position

Givaudan’s financial position improved further at the end of 2025. Net debt at December 2025 was CHF 3,678 million, compared to CHF 4,002 million in December 2024. The net debt to EBITDA ratio ⁵ was 2.1, compared to 2.3 in December 2024 and 2.5 in June 2025.

Dividend proposal 

At the Annual General Meeting on 19 March 2026, Givaudan’s Board of Directors will propose a cash dividend of CHF 72.00 per share for the financial year 2025, an increase of 2.9% versus 2024. This is the twenty-fifth consecutive dividend increase following Givaudan’s listing at the Swiss stock exchange in 2000.

Non-financial performance

Givaudan has made further progress in 2025 towards its ambitious ESG targets, which are fully aligned with the Group’s purpose.

Regarding its nature ambition and target to achieve net-zero greenhouse gas emissions across its value chain by 2045, the Group has attained a 50% reduction in scope 1+2 emissions compared with the 2015 baseline, while stabilising scope 3 emissions despite the constant growth of the Company. In addition, in 2024, Givaudan met its target of converting its entire electricity supply to fully renewable sources, a target originally set for 2025.

The Group also continues to make progress on its people-related targets in diversity and inclusion and, in 2025, has 34% of senior leadership positions held by women.

Givaudan is advancing towards its goal of sourcing all materials and services in a way that protects people and the environment by 2030, achieving 87% of its naturals portfolio sourced responsibly in 2025, up from 85% achieved in 2024. In line with this commitment and through broader community initiatives, the Group positively impacted around 820,000 people in the communities where it operates, an increase from 626,000 compared to 2024.

2025 Strategy: Targets exceeded

Our 2025 strategy ‘Committed to Growth, with Purpose’, was our intention to deliver growth in partnership with our customers, through creating inspiring products for happier, healthier lives and having a positive impact on nature, people and communities.

Givaudan exceeded its ambitious financial targets. With an average like-for-like sales growth of 6.8% for the period 2021–2025, Givaudan exceeded the upper end of its average five-year sales growth target of 4–5% on a like-for-like basis. And, with an average free cash flow of 12.5% for the period 2021–2025, the Company achieved its target of a free cash flow of at least 12%, also measured as an average over the five-year period strategy cycle.

2030 Guidance: Driving sustainable growth with customers 

Over the next five years, the Company aims to thrive in a dynamic market environment, driving sustainable growth with customers through creative, high value-added products and solutions that consumers love and that stand the test of time.

In this strategic cycle, Givaudan will leverage its existing strengths and proven business model in its core business, while further expanding into high-value adjacent spaces to fuel future sustainable and profitable growth. Remaining committed to its purpose of ‘Creating for happier, healthier lives with love for nature’, the Company will focus on three growth drivers and three growth enablers to deliver both financial and non financial value.

The Company is targeting 4–6% average like-for-like sales growth and over 12% average free cash flow over the five-year period and its 2030 purpose goals in the areas of nature, people and communities. This includes reducing scope 1+2+3 GHG emissions in line with the SBTi Net-Zero Standard trajectory and sourcing all materials and services in a way that protects the environment and people by 2030. The Company will also continue to pursue strategic acquisition opportunities that align with its strategic focus areas.

Further information 

The 2025 full year reports can be downloaded on www.givaudan.com › investors › financial results › results centre: 2025 Integrated Report on economic and ESG performance; 2025 Governance, Compensation and Financial Report.

Further information and reconciliations of the Group’s Alternative Performance Measures can be found in the Appendix of the 2025 Financial Report.

A conference call will be broadcast on www.givaudan.com on Thursday 29 January 2026 at 11:00 CET.

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Upcoming Company events
Annual General Meeting – 19 March 2026
First quarter sales – 14 April 2026
Half year results – 23 July 2026
Nine month sales – 13 October 2026

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Contact
Claudia Pedretti, Head of Investor and Media Relations
T +41 52 354 01 32
E claudia.pedretti@givaudan.com
 

Key tables

KEY FIGURES

In millions of Swiss francs except for earnings per share data	2025	2024	% change in CHF
Group sales	7,472	7,412	0.8%
– Fragrance & Beauty sales	3,830	3,660	4.6%
– Taste & Wellbeing sales	3,642	3,752	–2.9%
Like-for-like sales growth 1	5.1%	12.3%
Gross profit	3,252	3,271	–0.6%
– as % of sales	43.5%	44.1%
EBITDA 2	1,751	1,765	–0.8%
– as % of sales	23.4%	23.8%
Operating income	1,381	1,394	–0.9%
– as % of sales	18.5%	18.8%
Net income	1,071	1,090	–1.7%
– as % of sales	14.3%	14.7%
Operating cash flow	1,512	1,625	–7.0%
– as % of sales	20.2%	21.9%
Free cash flow 4	1,053	1,158	–9.1%
– as % of sales	14.1%	15.6%
Net debt (at 31 December)	3,678	4,002	–8.1%
Net debt/EBITDA 5	2.1	2.3
Earnings per share – basic (CHF)	116.08	118.17	–1.8%

FULL YEAR PERFORMANCE – JANUARY TO DECEMBER

		2025			2024
In millions of Swiss francs		Group	Fragrance & Beauty	Taste  & Wellbeing	Group	Fragrance & Beauty	Taste  & Wellbeing
Sales as reported		7,472	3,830	3,642	7,412	3,660	3,752
– growth in CHF	%	0.8%	4.6%	–2.9%	7.2%	10.5%	4.1%
– like-for-like 1	%	5.1%	7.9%	2.4%	12.3%	14.1%	10.7%
Acquisition impact (net)(A)		53	55	–2	43	54	–11
– acquisition impact (net)	%	0.7%	1.5%	–0.1%	0.6%	1.6%	–0.3%
Currency effects		–370	–174	–196	–399	–175	–224
– currency effects	%	–5.0%	–4.8%	–5.2%	–5.7%	–5.2%	–6.3%
EBITDA as reported 2		1,751	985	766	1,765	985	780
– EBITDA as reported	%	23.4%	25.7%	21.0%	23.8%	26.9%	20.8%
– Acquisition, restructuring expenses and project related expenses(B)		–39	–31	–8	–51	–32	–19
Louisville accident expenses		–17		–17
EBITDA comparable 3		1,807	1,016	791	1,816	1,017	799
– EBITDA margin	%	24.2%	26.5%	21.7%	24.5%	27.8%	21.3%

1. Acquisitions and divestments

	2025			2024
January to December In millions of Swiss francs	Group	Fragrance & Beauty	Taste  & Wellbeing	Group	Fragrance & Beauty	Taste & Wellbeing
Acquisitions and divestments	53	55	–2	43	54	–11
– Belle Aire Creations	5	5
– Vollmens Fragrances	10	10
– b.kolormakeup & skincare	40	40		48	48
– Amyris				6	6
– Discontinued and disposed business	–2		–2	–11		–11

2. Acquisition, restructuring and project related expenses incurred of CHF 39 million are largely related to costs incurred for the competition authorities investigations into the Fragrance industry, as well as some remaining costs for footprint optimisation.

SALES PERFORMANCE – OCTOBER TO DECEMBER

		2025			2024
Quarter only In millions of Swiss francs		Group	Fragrance & Beauty	Taste  & Wellbeing	Group	Fragrance & Beauty	Taste  & Wellbeing
Sales as reported		1,729	907	822	1,768	878	890
– growth in CHF	%	–2.2%	3.3%	–7.6%	7.1%	9.6%	4.8%
– growth like-for-like 1	%	3.2%	7.6%	–1.1%	10.1%	9.7%	10.5%
Acquisition impact (net)(A)		12	13	–1	26	28	–2
– acquisition impact (net)	%	0.7%	1.5%	–0.1%	1.6%	3.5%	–0.2%
Currency effects		–108	–51	–57	–75	–29	–46
– currency effects	%	–6.1%	–5.8%	–6.4%	–4.6%	–3.6%	–5.5%

1. Acquisitions and divestments

	2025			2024
Quarter only In millions of Swiss francs	Group	Fragrance & Beauty	Taste  & Wellbeing	Group	Fragrance & Beauty	Taste  & Wellbeing
Acquisitions and divestments	12	13	–1	26	28	–2
– Belle Aire Creations	5	5
– Vollmens Fragrances	8	8
– b.kolormakeup & skincare				28	28
– Amyris
– Discontinued and disposed business	–1		–1	–2		–2

 

SALES PERFORMANCE BY BUSINESS ACTIVITY

	2025	2024
January to December in %	Sales growth LFL 1	Sales growth LFL 1
Fragrance & Beauty	7.9%	14.1%
– Fine Fragrance	18.3%	18.4%
– Consumer Products	6.8%	13.5%
– Fragrance Ingredients and Active Beauty	–1.4%	11.1%
Taste & Wellbeing	2.4%	10.7%
– Europe	2.6%	5.9%
– South Asia, Middle East and Africa	7.8%	20.9%
– North America	3.0%	5.5%
– Latin America	0.7%	27.3%
– Asia Pacific	–0.8%	8.8%

 

SALES PERFORMANCE BY GEOGRAPHY

		2025			2024
January to December In millions of Swiss francs		Sales reported	LFL1 %	CHF %	Sales reported	LFL1 %	CHF %
LATAM		832	3.6%	–4.8%	875	26.1%	3.4%
APAC		1,798	5.0%	–1.3%	1,821	11.4%	7.2%
NOAM		1,712	2.6%	–1.3%	1,734	5.9%	4.8%
EAME		3,130	7.0%	5.0%	2,982	12.6%	9.8%
High growth markets		3,673	8.0%	6.3%	3,456	19.5%	9.7%
Mature markets		3,799	2.4%	–4.0%	3,956	6.4%	5.1%
Total Group		7,472	5.1%	0.8%	7,412	12.3%	7.2%

Client Alert  |  January 29, 2026

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Effective 1 February 2026, the Saudi Capital Market Authority (CMA) will open the Saudi capital market to all categories of foreign investors, eliminating the Qualified Foreign Investor (QFI) and swap-based access frameworks. These reforms, together with updated controls on real estate ownership by listed vehicles, mark a significant step in the continued liberalization and international integration of the Saudi capital markets.

The Capital Market Authority of the Kingdom of Saudi Arabia (CMA) has announced amended Rules for Foreign Investment in Listed Securities (the Rules) that will take effect on 1 February 2026, and will open the Saudi capital market to all categories of foreign investors and enable them to invest directly in listed securities. The amended Rules eliminate the Qualified Foreign Investor (QFI) construct for the Main Market and remove the swap-agreement framework previously used to provide synthetic economic exposure to Saudi listed securities, replacing these mechanisms with a unified regime for direct foreign participation.

Historical Background

Saudi Arabia has moved steadily from a tightly controlled foreign participation model toward broader market access, with foreign investment expanding in a phased and deliberately calibrated manner since the introduction of the QFI framework in 2015. Early inflows were modest, reflecting stringent eligibility requirements, foreign ownership caps and reliance on indirect exposure mechanisms (swap agreements), which together kept foreign ownership levels below those of global peers despite the size of the Saudi market. Foreign investor participation gained momentum following Saudi equities’ inclusion in major global indices (including MSCI, FTSE Russell and S&P Dow Jones), anchoring more durable, index-linked foreign allocations, particularly into large-cap, liquid issuers. In recent years, the CMA continued to ease access through incremental regulatory and operational reforms, supporting steady growth in foreign ownership; by end of Q3-2025, foreign investors held over SAR 590 billion (approximately USD 157 billion) of Saudi equities.

Opening the Market: Key Features of the Amended Foreign Investment Rules

Pursuant to the amended Rules, the CMA has eliminated the QFI framework and removed the swap-based access model, allowing a broader category of foreign investors to hold listed securities directly. Under the QFI framework, foreign investors had to go through an assessment by a licensed capital market institution to ensure eligibility and compliance on an ongoing basis with eligibility requirements, which included having a minimum of SAR 1.875 billion (US$500 million) of assets under management. The previous reliance on status-based access channels has been replaced by a unified framework under which all foreign investors may participate directly, subject to certain continuing ownership limits, while preserving a separate route for Foreign Strategic Investors (FSIs).

Existing foreign ownership limits remain in force, including the following:

• Non-resident foreign investors (other than FSIs) may not own 10% or more of any listed issuer’s shares or convertible debt instruments.
• Aggregate foreign ownership (all categories of resident and non-resident foreign investors, excluding FSIs) of any listed issuer’s shares or convertible debt instruments may not exceed 49%.
• Company constitutional limits, sector-specific restrictions and other regulatory caps continue to apply.

The Rules preserve a distinct regime for FSIs, but still condition the FSI’s investment in a listed company’s shares by a longer-term investment horizon (i.e., minimum of two years with a corresponding lock-up period) with the purpose of contributing in promoting the company’s financial or operational performance.  However, the Rules do not set any minimum or maximum targeted ownership thresholds for FSIs.

The CMA has also clarified that capital market institutions holding the portfolio accounts of foreign investors, as well as foreign investors share responsibility for monitoring compliance with lock-up periods and ownership limits, and that breaches may result in corrective measures and sanctions under the Capital Market Law, including orders to remedy or unwind non-compliant holdings.

Real Estate Controls: Parameters for Foreign Ownership by Listed companies, Funds, and Special Purpose Entities (SPEs)

Saudi Arabia has recently enacted a new Law of Real Estate Ownership by Non-Saudis, modernizing and liberalizing the framework for foreign ownership of real estate in the Kingdom by permitting non-Saudi individuals and entities to own property subject to defined conditions and regulatory oversight. This represents a notable shift from historically restrictive ownership regimes.

Against this backdrop, the CMA has approved Controls on the Ownership of Real Estate in the Kingdom by Listed Companies, Investment Funds and Special Purpose Entities (the Controls). The Controls establish the conditions under which listed companies, investment funds and special purpose entities may own real estate or other rights in real estate in the Kingdom, including in Makkah and Madinah, subject to specific safeguards. In particular:

• As a general rule, where listed companies own real estate in Makkah or Madinah, such properties must be used as the company’s headquarters or branch headquarters.
• Exceptionally, listed companies may own such properties for other purposes provided that (i) no FSI holds any shares or convertible debt instruments at any time, and (ii) aggregate ownership by non-Saudi natural and legal persons does not exceed 49% of the company’s shares or convertible debt instruments.

The Controls also permit capital market institutions to accept subscriptions from non-Saudis in investment funds that invest partly or wholly in Saudi real estate, including in Makkah and Madinah, subject to compliance with the Real Estate Ownership by Non-Saudis Law and its implementing regulations, including upon in-kind redemptions, termination or liquidation.

Expected Effect on the Saudi Capital Market

The CMA’s recent reforms form part of a broader, long-term program to attract international capital, enhance global index inclusion and deepen liquidity in the Saudi capital markets. In particular:

• Strategic policy alignment: The market opening closely aligns with Vision 2030’s objective of positioning Saudi Arabia as a global investment hub, strengthening the depth, competitiveness and international integration of the Saudi Exchange.
• Improved market liquidity: Broader direct access is intended to expand the investable foreign base, supporting increased capital inflows, enhanced liquidity and greater market depth, with potential positive effects on price discovery and execution efficiency.
• Simplified market access: By removing the QFI gateway and swap-based access models, the CMA has materially simplified foreign entry into the market, reducing operational, regulatory and compliance hurdles for global asset managers, custodians and intermediaries.
• Greater index and institutional appeal: The simplified access framework is expected to improve the market’s “indexability” and attractiveness to large institutional and benchmark-driven investors, reinforcing Saudi Arabia’s standing within global equity indices and facilitating more scalable, stable and durable foreign participation over time.

In parallel, the CMA has continued to expand the range of investable products available in the Kingdom. In July 2025, the CMA approved a framework for the issuance of Saudi Depositary Receipts (SDRs), enabling foreign companies to register and offer depositary receipts representing shares listed abroad on the Saudi Exchange. SDR issuers are subject to ongoing disclosure and continuing obligations, creating a regulated mechanism for international issuers to access Saudi capital without pursuing a primary listing in the Kingdom. Together with the January 2026 market-opening reforms, the SDR framework supports deeper capital markets, diversified product offerings and enhanced cross-border investment flows.

Looking Ahead

The CMA has also publicly indicated that the foreign ownership limits will be reviewed in 2026, signaling the potential for further liberalization following the initial market opening.  We expect these measures to continue enhancing Saudi Arabia’s capital formation environment, while preserving safeguards designed to protect market integrity and national regulatory priorities.

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The following Gibson Dunn lawyers prepared this update: Najla Al-Gadi, Ibrahim Soumrany, and Sara Almahayni.

Gibson Dunn’s lawyers are available to assist in addressing any questions you may have regarding these developments. If you wish to discuss any of the matters set out above, please contact the Gibson Dunn lawyer with whom you work or any member of Gibson Dunn’s Capital Markets team, including the following members in Riyadh:

Najla Al-Gadi (+966 53 993 9069, nalgadi@gibsondunn.com)

Ibrahim Soumrany (+966 55 798 9799, isoumrany@gibsondunn.com)

© 2026 Gibson, Dunn & Crutcher LLP.  All rights reserved.  For contact and other information, please visit us at www.gibsondunn.com.

Attorney Advertising: These materials were prepared for general informational purposes only based on information available at the time of publication and are not intended as, do not constitute, and should not be relied upon as, legal advice or a legal opinion on any specific facts or circumstances. Gibson Dunn (and its affiliates, attorneys, and employees) shall not have any liability in connection with any use of these materials.  The sharing of these materials does not establish an attorney-client relationship with the recipient and should not be relied upon as an alternative for advice from qualified counsel.  Please note that facts and circumstances may vary, and prior results do not guarantee a similar outcome.

A series of papers published in JACC: Heart Failure provide new findings from the EMPOROR-Preserve trial and several other reports that could benefit the management and treatment of patients with heart failure (HF), specifically those with HF with preserve ejection fraction (HFpEF). Additionally, an expert commentary provides insights into subgroups and special populations in HF clinics.

In EMPOROR-Preserve, original research from João Pedro Ferreira, MD; Milton Packer, MD, FACC; and Javed Butler, MBBS, FACC, et al., reveals patients with HF with mildly reduced ejection fraction (HFmrEF) or HFpEF who had higher serum magnesium levels had lower risk of primary outcomes events if receiving empagliflozin (10 mg/daily) compared with those receiving placebo.

The study randomized nearly 6,000 patients to either empagliflozin or placebo, with lab results available at baseline, weeks 4, 12, 32 and 52 and then every 24 weeks thereafter for a median follow-up of 26 months. The primary outcome was a composite of cardiovascular death or HF hospitalization.

Overall results found that patients receiving placebo experienced a higher risk of primary outcome events with higher magnesium levels, while empagliflozin was associated with a greater reduction of primary outcome events at higher baseline magnesium levels. Researchers also noted that patients with higher magnesium levels were older, had lower estimated glomerular filtration rate, and higher prevalence of atrial fibrillation, while patients with lower serum magnesium had diabetes and more frequently used thiazide-type diuretic agents.

According to the authors: “The role of magnesium in HFmrEF/HFpEF should be investigated further, particularly how the use of SGLT2 inhibitors may increase magnesium levels and how this influences the function of cardiomyocytes, endothelial cells, and autophagic flux.”

In a related editorial comment, Wendy McCallum, MD, et al., highlight that “one of the key messages” from EMPOROR-Preserved is the fact “there was no suggestion of harm with randomization to SGLT2 inhibitor irrespective of baseline magnesium level.” They write that “Ferreira et al., have generated an interesting hypothesis that SGLT2 inhibitors are associated with a differing degree of cardiovascular benefit depending on the baseline magnesium level” and suggest that “future studies to evaluate the role of magnesium and change in magnesium in relation to SGLT2 inhibitor treatment among patients with HFrEF and with HFmrEF/HFpEF are needed.”

Three separate “brief reports” explored other areas of HFpEF management. In one, authors Yu Kang, MD, et al., assessed whether left ventricular (LV) end-diastolic dimension (LVEDD) is a prognostic metric in HFpEF. Their findings suggest yes, with results showing a “showing a U-shaped relationship with mortality” at one-year post-discharge. According to the authors, patients with LVEDD beyond 45 to 59 mm had increased risks of death and distinct clinical characteristics. “This concept of phenotyping HFpEF by LV remodeling could be considered in future clinical practice and research,” they write.

In another report, Silav Zeid, MSc, et al., shared the results of the MyoMobile Trial, which found that an app-based adaptive digital coaching intervention increased daily step count in patients with HFpEF compared with standard of care or physical activity tracking alone. The findings “support integrating digital coaching into routine HFpEF care and warrant future studies to explore long-term effectiveness, broader implementation and potential influences on functional capacity and clinical outcomes,” they said.

In a third brief report, Oluwapeyibomi I. Runsewe, MD, et al., assessed whether use of retinal optical coherence tomography angiography (OCTA) could capture early microvascular changes in HFpEF that are associated with cardio-renal dysfunction. Their results found that retinal OCTA imaging is feasible “in identifying retinal microvascular dysfunction in the form of lower vessel densities (especially for the SCP on 3 × 3 mm angiograms) as a distinct phenotype of HFpEF associated with greater cardiorenal impairment,” and they suggest that “further investigations are warranted to establish the proposed thresholds of retinal OCTA metrics that can noninvasively identify patients with HFpEF and microvascular dysfunction.”

Outside of HFpEF treatment, “leading edge commentary” from Vanessa Blumer, MD, FACC; Biykem Bozkurt, MD, PhD, FACC; and Marvin A. Konstam, MD, FACC, et al., offers an update from the Heart Failure Collaboratory and the Heart Failure Collaboratory Academic Research Consortium Expert Consensus Panel on “consensus definitions and considerations to better characterize subgroups and special populations, supporting more precise, relevant, and patient-centered trial design.”

“In the evolving landscape of HF management, the identification and analysis of subgroups and special populations within clinical trials are crucial for enhancing clinical decision-making, guiding further research, and understanding heterogeneity in study outcomes,” the authors write. “…These efforts aim to improve the evaluation of therapeutic interventions, inform regulatory decision-making, and advance personalized care across the spectrum of HF.”

Specifically, the statement calls on policymakers to address “the root causes of environmental stressors” and adopt stricter air quality and noise standards, phase out fossil fuels and regulate toxic chemicals. It also highlights several health system adaptation and resilience measures, including implementation of public awareness campaigns, health care workforce training and retention strategies, data sharing and interoperability, dedicated funding for health system adaptation and crisis response, investment in telemedicine and integrated care models, and more.

“Research has shown the negative health impacts of pollution, noise, rising temperatures and other environmental stressors,” says ACC President Christopher M. Kramer, MD, FACC. “The time for action on addressing the impact of the environment on cardiovascular health is now and essential to reducing the burden of cardiovascular disease around the world.”

Introduction

Background

The demand for advanced medical imaging services continues to grow at a rapid pace, and reducing time delay from image capture to radiological reporting remains a priority for public hospital services [,]. Radiologists often need to work through large volumes of information to report on medical images for a wide array of patient groups. This can be challenging in clinical environments with constraints on time, resources, and related workflow pressures. Excessive delay from time of medical image capture to the provision of a definitive radiological report to the referring clinical team can undermine the quality and safety of health care delivery [,].

Medical imaging has been, and remains, at the forefront of advancements in digital health technologies in everyday clinical practice [,]. Consequently, there is an array of digital tools available to support radiology decision-making that have arisen from advances in machine learning and other related technologies. Artificial intelligence (AI) algorithms that are commercially available, and with regulatory approvals already in place, are now being embedded in digital tools and readily adopted into routine practices for radiologists in various settings internationally [,].

In experimental and validation studies, AI algorithms show strong technical performance: meta-analyses report pooled sensitivity and specificity values exceeding 0.80-0.85 for tumor metastasis and rib fracture detection, with the mean area under the curve near 0.90 [,]. These results highlight the potential for AI to enhance accuracy and throughput, particularly in resource-constrained health systems [,,]. Yet this technical promise has outpaced the evidence on real-world implementation []. The literature remains dominated by model validation [] and cross-sectional studies of clinician trust [-], with few studies examining how AI systems are adopted, adapted, or sustained within the operational realities of hospital environments. Recent reviews emphasize this gap, noting that most AI-radiology research ends at performance benchmarking and fails to explore workflow integration, organizational readiness, or long-term routinization [,]. Broader governance and workforce analyses likewise underline persistent uncertainty around accountability, medicolegal responsibility, and system-level preparedness for AI-supported care [,]. Collectively, these gaps constrain understanding of how algorithmic potential translates into clinical and organizational value.

While these quantitative evaluations and meta-analyses have established AI’s diagnostic capability, they provide little insight into how and why such technologies succeed or fail once introduced into routine clinical practice. Many rely on retrospective datasets, simulated environments, or controlled reader studies that remove the influence of real-world complexity [-]. Consequently, they overlook the macro-, meso-, and microlevel dynamics of workflow adaptation, human-technology interaction, and organizational and sociocultural context that determine whether AI enhances or disrupts practice. A qualitative implementation approach is therefore critical for exploring the lived experiences, informal workarounds, and contextual contingencies that shape integration in situ [,]. Such an approach complements quantitative evidence by revealing the social and organizational mechanisms through which AI adoption is negotiated, sustained, or resisted in everyday radiology work and practice.

Emerging qualitative and mixed methods studies have begun to address aspects of these challenges by exploring radiologists’ perceptions, sources of trust and mistrust, and organizational barriers to adoption [,]. However, most have relied on single-time-point interviews, limited samples (n<20), or hypothetical case vignettes that do not capture the evolving interaction between users, workflows, and technology over time [,,]. Few have been conducted within active service settings or have systematically linked individual experiences to organizational processes or system-level factors [,]. This has resulted in a descriptive but fragmented evidence base that provides limited insight into how implementation unfolds, stabilizes, or falters once AI becomes part of routine care.

This study responds directly to that gap by presenting a qualitative, end-to-end evaluation of AI implementation within a large tertiary radiology department in Brisbane, Queensland, Australia. Here, end-to-end refers to a lifecycle approach spanning predeployment context and readiness, peri-implementation adaptation, and postimplementation integration and routinization, examining how technological, human, and organizational factors interact over time [,]. There is a broad range of implementation frameworks to assess the implementation of a digital health innovation across a life cycle; however, they are varied in their analytical purpose []. Strategy-based models such as the Expert Recommendations for Implementing Change (ERIC) framework provide detailed lists of discrete implementation actions, but they are limited in explaining the mechanisms through which adoption unfolds in complex clinical environments []. In contrast, our evaluation sought to understand how and why AI integration succeeds or stalls within a dynamic, real-world system. The nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework [] was therefore selected to guide data collection and analysis. NASSS offers a theoretically grounded structure for examining the sociotechnical complexity of digital innovation by integrating the domains of technology, adopters, organization, value proposition, and wider context [,]. This systems-oriented lens provides greater explanatory power than more generalized implementation approaches for capturing interdependencies, contextual contingencies, and the temporal evolution of barriers and enablers across the implementation lifecycle.

By situating implementation within real-world clinical operations rather than experimental or hypothetical conditions, this study provides a rare longitudinal perspective on how AI becomes normalized or resisted within a complex hospital environment. Its findings have direct relevance to current policy efforts to scale AI responsibly in public health systems, where efficiency, safety, and governance imperatives converge [].

Aims

Accordingly, this paper aims to examine the real-world implementation of an AI-based clinical decision support tool in radiology through an end-to-end qualitative evaluation across pre- (baseline), peri-, and postimplementation phases. Specifically, it seeks to identify the key contextual, organizational, and human factors shaping adoption and sustainability, to map these influences using the NASSS framework, and to generate insights that inform evidence-based strategies and policy for integrating AI safely and effectively into public hospital imaging services.

Methods

Study Design and Theoretical Framework

This study used a qualitative prospective design. The study was structured across 3 temporal phases to capture the evolving context of AI implementation within the radiology department.

The preimplementation or baseline phase (12 months before deployment) corresponded to the period when the AI tool had not yet been introduced. This phase reflected baseline organizational conditions, established workflows, and prevailing attitudes toward digital tools in radiology.

The peri-implementation phase (an 8-week transition period) covered the initial rollout of the AI system and its integration into existing digital and reporting infrastructure. This period was characterized by early interaction with the tool and short-term adaptation of work processes.

The postimplementation phase (12 months after deployment) represented a period of stabilization in which the AI tool had become part of routine operations. This phase captured the mature context of use, reflecting how the technology was embedded, maintained, and normalized within everyday practice.

The reporting of this study was in alignment with the COREQ (Consolidated Criteria for Reporting Qualitative Studies; ).

NASSS Framework

The NASSS framework was used to inform the study design and interview questions. The NASSS framework provides a systematic foundation for examining challenges across multiple domains and their dynamic interactions, which may influence the uptake, implementation, outcomes, and sustainability of technology-supported health programs []. It facilitates consideration of how various factors interact and adapt over time, influencing success, and includes the following domains:

1. Condition or illness: the nature and complexity of the condition being addressed.
2. Technology: the specific technology being implemented.
3. Value attributed to the technology: the perceived benefits and utility of the technology.
4. Individual adopters: the clinicians and patients using the technology.
5. Organizational adopters: the health care organizations implementing the technology.
6. External context: the broader context, including regulatory, economic, and social factors.

Setting

This was a single-site study conducted at a large public tertiary referral hospital in Brisbane. The hospital’s Medical Imaging Department offers a comprehensive range of diagnostic imaging services to support patient care across various medical specialties.

AI Clinical Decision Support System

The technology adopted by the department is a third-party and commercially available multiorgan AI-based computerized clinical decision support system (CDSS) for radiologists. The CDSS uses multiple specialized convolutional neural networks across the entire machine learning cycle, including preprocessing, candidate generation, classification, and postfiltering. It has been classified and approved as a diagnostic tool under the current Australian Therapeutic Goods Administration regulatory framework. The decision support system integrates with existing medical imaging hardware and software to allow computed tomography (CT) images to be automatically transferred from the scanner, preprocessed, and prepared for interpretation by radiologists. The CDSS flags or highlights any issues within the CT image that require further differential diagnosis by the radiologists. In 2021, before full site implementation, the study site tested this tool among a small group of radiologists (n=4) and anecdotally reported positive experiences.

Participant Recruitment and Sampling

Participants included radiology consultants, registrars, and radiographers employed within the Medical Imaging Department who were involved in chest CT reporting during the study period. Recruitment was undertaken via internal email by an embedded chief investigator. A purposive yet stratified sampling approach was used to achieve broad representation across professional roles and levels of seniority. At the time of data collection, the lead interviewer (SN) was an experienced PhD-trained male implementation science researcher with no supervisory, managerial, or clinical authority over participants. SN had established professional familiarity with the department through earlier collaborative work, but no direct reporting relationships. Participants were informed of SN’s research role, disciplinary background, and interest in understanding real-world implementation challenges before interview commencement. Stratification was guided by the departmental organizational chart to ensure inclusion of participants from different functional areas and reporting responsibilities. This approach sought to capture a range of experiences across the implementation process rather than statistical representativeness. Sampling continued iteratively across the pre- (baseline), peri-, and postimplementation phases until thematic adequacy was reached, indicated by repetition of key concepts and no emergence of new issues in subsequent interviews [].

Qualitative Interviews

Semistructured face-to-face interviews, approximately 40 minutes in length, were conducted by an experienced implementation science researcher (SN) according to participant preference (in person, via Microsoft Teams, or through phone; ). All interviews were audio-recorded and transcribed upon participant consent. Interviews were conducted flexibly, with questions adapted to participant roles and experience. Not all questions or prompts were asked in every interview, but the guide provided a consistent framework to ensure coverage of key domains. The interviewer kept reflexive notes after each interview to document emerging impressions, relational dynamics, and potential influences of their positionality on data generation.

Study Materials

We used a reflexive framework method to guide the development of a semistructured interview template, aligning with our study aims [,]. This approach aimed to capture a comprehensive range of insights, perceptions, and experiences, providing a rich dataset for analysis.

Data Analysis

Interview transcripts were analyzed using an iterative, multistage process combining thematic analysis with NASSS-informed framework mapping [-]. Before analysis, the research team discussed their disciplinary positions and assumptions regarding AI in radiology, documenting these reflections to support analytic reflexivity. SN led coding with AE providing independent review; neither had clinical authority over participants.

Analysis and data collection were conducted concurrently to guide purposive sampling and determine saturation. Early transcript review enabled the identification of preliminary codes and gaps, informing subsequent recruitment to ensure variation in experience and role. No participant reviewed the transcripts or findings before publication, consistent with the exploratory and ecological design of the study. However, findings were discussed with senior departmental clinicians and technical leads during routine project meetings. These discussions did not involve revising data or themes but served to ensure that the interpretations accurately reflected the broader organizational context and the realities experienced within the department. This informal sense-checking supported contextual validity while maintaining analytic independence.

Initial inductive coding was undertaken by one researcher (SN), who read each transcript line by line and assigned short descriptive phrases summarizing perceived barriers, facilitators, or neutral factors related to AI implementation. To strengthen analytic credibility, a second researcher (AE) independently reviewed a subset of transcripts and the draft codebook. Coding discrepancies and interpretive differences were discussed and resolved through consensus, providing a form of cross-checking without imposing rigid interrater reliability metrics. Iterative discussions across the research team further refined code boundaries, ensuring conceptual coherence and maintaining an audit trail of analytic decisions.

Once inductive coding was complete, higher-order categories were developed to capture recurrent concepts and relationships. These categories were then deductively mapped to the NASSS framework. This process enabled systematic classification of determinants by domain (eg, technology, organization, value proposition, adopters, wider system, and clinical context) while retaining sensitivity to context-specific nuances. The mapping process was iterative, with subthemes revisited and refined to ensure conceptual alignment between inductive insights and NASSS constructs, and to account for determinants that spanned multiple sociotechnical domains. Mapped subthemes were subsequently synthesized into a set of higher-order, cross-cutting determinants representing the dynamic interactions between technological, organizational, and adopter-related factors across implementation phases. This synthesis informed the structure of the results, where inductive findings and NASSS categories are integrated to illustrate how determinants evolved from baseline to peri-implementation and into routine use. Illustrative quotes were selected by consensus to exemplify the range of perspectives within each theme and subtheme. Quote selection focused on demonstrating variation, depth, and temporal evolution rather than providing isolated examples, ensuring that quotations functioned as analytic evidence and supported the integration of inductive insights with NASSS domains. An accompanying summary table provides an at-a-glance depiction of how inductive themes aligned with specific NASSS domains and subdomains, further enhancing analytic transparency and coherence.

To enhance analytic transparency, a content count of all coded barriers and enablers was compiled in Microsoft Excel and stratified by implementation phase (baseline, peri-implementation, and postimplementation). This numerical summary illustrated the distribution and relative prominence of determinants across NASSS domains (eg, technological, organizational, and adopter-related), providing a structured complement to the qualitative narrative. The count functioned as a descriptive aid to visualize patterns within the dataset, highlight areas of convergence and divergence across phases, and support the organization of complex, multilevel determinants [].

Trustworthiness

To ensure trustworthiness, the research team engaged in continuous reflexive discussions throughout data collection and analysis, critically examining how their disciplinary backgrounds and assumptions could shape interpretation. Coding decisions were documented in an evolving analytic log, forming an audit trail that supported transparency and replicability. Regular peer debriefings were held to resolve interpretive differences and refine theme definitions. Trustworthiness was further reinforced through the systematic application of the NASSS framework, which provided a theoretically grounded lens for organizing inductive findings. The inclusion of a quantitative content count of coded determinants added descriptive transparency, demonstrating how interpretations were anchored in the underlying data distribution. Together, these strategies strengthened the credibility, confirmability, and dependability of the qualitative findings [,].

Ethical Considerations

The Human Research Ethics Committee granted ethical clearance for this research (HREC/2021/QMS/81483). All participants provided written and verbal informed consent before participating in the study. Participation was voluntary, and participants could withdraw at any time. Participants were assured that their responses would be confidential, would not be shared with departmental leadership in identifiable form, and would have no bearing on workplace evaluation or progression. No incentives were offered, and no previous personal relationships existed between the researcher and participants beyond professional familiarity.

Results

Participant Characteristics

A total of 43 one-on-one interviews were conducted across the study timeframe, as shown in . This consisted of 7 (16%) radiographers, 20 (47%) registrar radiologists, and 16 (37%) consultant radiologists. A total of 9 (21%) participants were interviewed across multiple time points, consistent with public health services experiencing regular staff rotation, shift-based work patterns, and competing clinical pressures. While this posed practical challenges to longitudinal participation, it was also indicative of practical challenges with AI implementation in hospital medical imaging departments. To accommodate this and maintain the integrity of the analysis, each interview was treated as a discrete data point. This allowed us to capture a wider range of perspectives from across the workforce and reflect the dynamic, high-turnover environment typical of public hospital settings.

NASSS Informed Barriers and Enablers of AI Implementation in Radiology

A total of 56 barriers and 18 enablers were identified at baseline, 55 barriers and 14 enablers during peri-implementation, and 82 barriers and 33 enablers at postimplementation. presents barriers across NASSS domains and study phases, while shows enablers across the same phases.

‎

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At baseline, organizational barriers were the most prominent, representing nearly half of all identified barriers (26/56, 46%). These are primarily related to limited technological readiness, insufficient training, and inadequate workflow planning for implementation. Technological barriers followed (12/56, 21%), reflecting early concerns about AI performance and output accuracy, while adopter-related barriers (7/56, 12%) centered on uncertainty regarding medicolegal accountability when using AI in reporting. The main enablers at this stage were found within the organizational (6/18, 33%) and value proposition (5/18, 28%) domains, reflecting a collegial, innovation-friendly culture and a belief in the technology’s potential for efficiency and time savings.

During the peri-implementation phase, technological barriers dominated (31/55, 56%), particularly those concerning interoperability and system performance. These were followed by organizational barriers (14/55, 25%) related to weak implementation planning and inadequate workflow support, and a smaller set of adopter barriers (4/55, 7%) linked to limited trust in the AI system. Despite these issues, several enablers emerged, most notably within the value proposition domain (7/14, 50%), where participants anticipated potential efficiency gains if technical and integration challenges could be addressed. A smaller number of enablers (4/14, 28%) related to technology, as some users began using the AI system to cross-check their own interpretations.

By postimplementation, technological barriers persisted (41/82, 50%) as problems with accuracy, reliability, and speed remained unresolved. Organizational barriers (18/82, 22%) continued to reflect deficiencies in communication, training, and workflow integration, while adopter barriers (13/82, 16%) indicated ongoing distrust in the AI and reluctance to incorporate it fully into routine practice. However, this phase also saw the most substantial growth in enablers (a total of 33), particularly within technology (22/33, 67%), as users adapted the system for use as a secondary check or safety mechanism. Additional enablers were identified within the value proposition (7/33, 21%), where participants recognized relative efficiency benefits, and among adopters (4/33, 12%) who expressed emerging, albeit cautious, trust in the AI’s evolving role.

Across all NASSS domains, implementation was characterized by an interplay between anticipated risks, such as workflow integration and information overload, and realized challenges during peri-implementation, many of which persisted into routine use (). While optimism and perceived value remained for some, trust and adoption were undermined by ongoing performance and communication barriers. Together, these patterns illustrate how implementation unfolded within a large, dynamic clinical service, with determinants shifting as the AI system moved from anticipation to early use and then attempts to move into routine practice. The relative prominence of technological, organizational, and adopter-related factors at each phase provides a contextual frame for understanding the subsequent themes. These distributions therefore situate the qualitative findings within the broader organizational and technological environment in which the AI was being implemented.

Framework Analysis and Narrative Synthesis of NASSS Domains and Subdomains

presents a synthesis of inductive themes mapped to the NASSS framework, illustrating how key implementation dynamics evolved across baseline, peri-implementation, and postimplementation phases. The table highlights temporal shifts in organizational readiness, technological integration, value perception, adopter engagement, and wider system influences. These findings are further expanded in the results narrative synthesis, offering deeper insight into the contextual and temporal nuances of implementation.

Organization

There were 3 inductive subthemes that mapped under the organization domain. There were challenges with sustained implementation planning, which mapped under the NASSS subdomain of “work needed to plan, implement, and monitor change;” relational engagement and communication, which mapped to “organizational readiness and capacity to innovate;” and workflow optimization, which mapped to “extent of change needed to organizational routines.”

Work Needed to Plan, Implement, and Monitor Change (Sustained Implementation Planning)

At baseline, participants expected limited planning and support for rollout, reflecting past experiences with digital systems. As one consultant noted:

You don’t actually discover issues or problems with that new process or software or whatever until you’re using it, and then often there’s a lack of support on a day-to-day kind of basis.
[P4, Consultant]

Feedback mechanisms were also described as weak, with another adding:

There’s no way for us to feed…I don’t know of a way for me to feed that back.
[P1, Consultant]

These comments illustrated low confidence in the organization’s ability to anticipate or respond to implementation challenges.

During peri-implementation, radiologists described minimal systematic planning or training.

Not training per se, I think there was one meeting where they said that it was being implemented.
[P20, Registrar]

Another reflected:

Not very well (when asked about implementation planning)…they haven’t really. Besides telling us that we’re going to put it into practice, yeah, there’s just not much that they’re saying about it.
[P22, Registrar]

Such experiences made participants cautious about the department’s readiness to adopt AI, with one consultant admitting:

I think in retrospect, we could have done more in terms of educating people.
[P21, Consultant]

Postimplementation reflections reinforced these concerns, highlighting the ongoing absence of structured improvement strategies to support uptake and sustainment. Participants were critical of the ad-hoc implementation process, arguing that:

If there are programs that are clinically usable and are planned to be rolled out within the department, then I think it makes sense for everyone to have formal training.
[P33, Registrar]

Even so, there was a strong appetite for more structured professional development in AI tools, with one consultant remarking:

I would like to be taught. I am a better learner if I’m taught.
[P32, Consultant]

Across phases, participants viewed planning and monitoring as reactive rather than anticipatory. Despite enthusiasm for AI, the lack of systematic preparation and ongoing learning opportunities constrained the department’s ability to embed change effectively. Organizational challenges were compounded by high staff turnover and rotating clinical rosters, which limited continuity of learning and reduced opportunities for cumulative familiarity with the system. These shifting workforce conditions shaped how planning gaps were experienced and helped explain some variation in engagement and confidence across the implementation period.

Organizational Readiness and Capacity to Innovate (Relational Engagement and Communication)

At baseline, participants described a workplace that was broadly supportive of new ideas but slow to coordinate change due to limited capacity. As one consultant noted, “in public systems, people just tend to put up with inefficiencies” (P1, Consultant). Such reflections suggested that innovation was encouraged in principle but rarely matched by structured communication or system support.

During rollout, participants described poor communication and limited coordination between teams.

Having [vendor redacted] in one corner, radiologists in another, and us talking together… takes resources to get everyone together.
[P23, Radiographer]

Another reflected postimplementation, “We didn’t actually tell most radiographers this was happening” (P41, Radiographer), highlighting the persistence of siloed and reactive coordination. Participants attributed emerging resistance partly to this fragmentation, explaining that “a lot of stuff was happening in the background with the PAX guys and the software people” (P43, Consultant).

Lack of early adopter involvement and unclear lines of responsibility were seen as weakening organizational readiness, even where enthusiasm for innovation remained high. This persisted into postimplementation, undermining the department’s sociotechnical capacity to support AI integration. As one consultant explained:

One of the issues is that people who understand computers are not the people who understand medicine, and vice versa. So, there’s probably a communications issue.
[P29, Consultant]

Participants linked these challenges to the organization’s limited capacity to learn from implementation, with one concluding:

We could have done more work with the implementation initially; there could have been more clinician involvement.
[P43, Consultant]

Across phases, the organization was perceived as open to innovation but constrained by weak communication channels and reactive coordination. Participants emphasized that the capacity to innovate depended less on enthusiasm than on the presence of structured dialogue, feedback loops, and shared ownership across clinical and technical teams. These relational and coordination challenges intersected with changing workload pressures and fluctuating departmental priorities, reinforcing that uptake was influenced not only by communication structures but by the broader organizational environment in which teams were continuously reconfigured.

Extent of Change Needed to Organizational Routines (Workflow Optimization)

Across phases, participants described the introduction of the AI tool as requiring substantial adjustments to established reporting routines. At baseline, senior radiologists viewed it as a potential aid to workflow optimization, especially in easing registrar workloads. As one consultant put it:

If we were able to create a system or facilitate more report completion… particularly for the registrars, that would increase satisfaction.
[P1, Consultant]

This optimism reflected expectations that automation would streamline repetitive elements of reporting rather than disrupt them.

During peri-implementation, participants described the tool as introducing extra steps and interruptions to normal work patterns. One consultant explained:

It does add to the amount of things you look at… you’ve got to report your CT as normal, and then you’ve got a bunch of other sequences to scroll through at the end. Not that it adds a lot, but yeah, it does… there’s more… people have been like, what’s all these extra images? They don’t really know what to do with it too much yet either.
[P18, Consultant]

This illustrates a lack of clear guidance or established routines for how to use or interpret the additional studies. This persisted at postimplementation:

I was sort of holding on to reports for a few hours before signing off because I didn’t want additional data to come through that I hadn’t looked up before signing off.
[P30, Consultant]

Instead of reducing workload, the new process created pauses, re-checks, and deferred sign-offs. Registrars similarly described difficulty maintaining rhythm and concentration, explaining that:

It’s hard to get a routine…you have to have a different routine in your workflow for a particular assessment.
[P26, Registrar]

These comments captured how the tool altered the flow of image review and report finalization, requiring constant recalibration of familiar sequences. Some radiologists had developed compensatory strategies to manage these disruptions; reordering tasks, batching reports, or consciously ignoring low-yield prompts. As one consultant reflected:

So for differentials, I go back to the normal scan because the tool obscures some details. So, it’s more I use it for identifications…. So, for me, it’s more identifying. Ok, there’s something there. I need to go back and check (normal scans).
[P31, Consultant]

Overall, participants described workflow change as cumulative and largely unplanned. The tool demanded continuous microadjustments rather than a one-time shift in practice. What emerged was a pattern of individual adaptation rather than coordinated redesign; clinicians modified existing routines to fit the tool rather than the tool being aligned with the established clinical workflow. The extent of workflow disruption experienced by clinicians also reflected the realities of a service marked by rotating staff, shifting caseloads, and variable daily pressures.

Technology

The NASSS technology domain is mapped with 2 inductive subthemes: extraneous data and information overload, aligning with the subdomain knowledge generated, and system performance and material integration, aligning with the subdomain material properties. Together, these themes captured how the technical characteristics of the AI tool shaped user experience, trust, and perceived value across implementation phases.

Knowledge Generated (Extraneous Data and Information Overload)

At baseline, a consultant anticipated risks of information overload based on previous exposure to commercial AI tools. One explained, “You could spend all day circling these things” (P5, Consultant), capturing early concerns that automated outputs might flood readers with marginal or irrelevant findings.

During peri-implementation, these concerns materialized as the system generated excessive, low-value information:

Too much data… You really want a traffic-light system.
[P25, Consultant]

Another registrar observed:

I’m not sure how many people look at it. It spits out so many images and random tables
[P20, Registrar]

These reactions pointed to an emerging pattern of signal-to-noise imbalance, where radiologists spent more time filtering artefacts than interpreting meaningful results. By postimplementation, some users described partial adaptation, learning to disregard redundant data or mentally triage the AI’s output.

It gets a little complicated when it picks up things that are artifacts. But yeah, I can work around it.
[P43, Consultant]

However, frustration persisted among others who saw the clutter as undermining efficiency rather than enhancing it:

It’s a waste of time. It’s just clutter, you know? … I usually ignore it.
[P37, Consultant]

Across phases, information overload remained one of the most salient barriers to adoption. While individual users developed coping strategies, these adaptations reflected workaround behavior rather than genuine integration, reinforcing perceptions that the AI’s knowledge output was not yet aligned with clinical reasoning or workflow needs.

Material Properties (System Performance and Material Integration)

Performance concerns were a defining feature of the AI’s reception, particularly during peri-implementation. A registrar characterized it bluntly as “Not very accurate. Just a splatter approach” (P17, Registrar), reflecting the perception that the system detected excessive findings without adequate specificity. Such errors eroded trust and reduced the incentive to incorporate its output into reporting routines.

By postimplementation, participants expressed more nuanced but still divided views. Some regarded the system as useful for reassurance or cross-checking:

Used it more like a check-off — especially when you have things that are complex, and there are a lot of findings.
[P30, Consultant]

Others found the persistent false positives distracting and demoralizing. As one put it:

For me to waste time looking at it…it’s circled this fecal matter in the splenic flexure.
[P28, Consultant]

Several participants emphasized that perceived technical performance shaped how often they engaged with the system at all. When lag, sensitivity issues, or interface friction increased, clinicians tended to bypass or ignore the tool. Over time, its role shifted from active decision aid to optional background reference, indicating a decline in both trust and functional value.

Interoperability problems surfaced most clearly during peri-implementation, where users described limited integration between the AI software, picture archiving and communication system (PACS), and reporting systems. One consultant explained:

That’s high-level stuff, right? That’s integrating the processing, postprocessing software with the reporting software. But we don’t have that capacity.
[P21, Consultant]

They further highlighted redundancy and excess image sets, noting “way too, way too many sequences…we need to distil that down.”

By postimplementation, interoperability was less salient; some technical issues with integrating the AI into the system appeared resolved, but residual inconsistencies persisted. As one registrar noted:

There’s not… uniformity to the sequences that are made. The order that they come out, …that’s different from scanner to scanner.
[P26, Registrar]

Availability also varied:

It’s not always there. So, you’ve got to sort of remember…to look for.
[P26, Registrar]

Display and PACS constraints continued to affect use:

I don’t like the way that gets displayed…that’s a PACS system…how the series [are] actually displayed.
[P26, Registrar]

Across phases, the material outputs of the AI, its sensitivity, specificity, and responsiveness, directly influenced its perceived usefulness. Participants consistently linked suboptimal performance to disengagement, showing that successful technological integration required not just accuracy, but reliability, responsiveness, and design alignment with radiologists’ expectations of diagnostic precision. Furthermore, while major interoperability barriers had eased, the system never fully aligned with the routine reporting infrastructure, leaving incompatibilities.

Across all technology-related subdomains, participants described a gap between what the AI produced and what clinicians could use. Information overload and variable system accuracy combined to erode trust and limit engagement. While technical adaptation occurred at the individual level, collective integration into practice remained constrained, signaling that technological refinement and interpretability are prerequisites for sustained adoption.

Value Proposition and Clinical Context

Across all phases, discussions of value proposition were less prominent than those of technology or organization, but two inductive subthemes mapped clearly to the NASSS value proposition domain: perceived benefits for clinical workload and safety, which mapped to demand-side value, and credibility and clarity of purpose, which mapped to supply-side value. These intersected closely with the evolving clinical context, in which fluctuating workload pressures and infrastructure challenges shaped how the AI’s value was interpreted.

Demand-Side Value (Perceived Benefits for Clinical Workload and Safety)

At baseline, participants viewed the AI as a potential solution to workload strain and reporting delays. Anticipated benefits were framed around efficiency, redistribution of tasks, and registrar support, signaling early optimism that automation would enhance throughput and safety. The department’s intense workload and frequent interruptions reinforced this demand-side appeal: as a consultant noted, AI might “make our job easier” (P1, Consultant).

During peri-implementation, optimism gave way to more conditional appraisals. While some identified benefits for prioritization, “It highlights a few cases that you can look at first. That’s useful when there’s a backlog” (P19, Registrar), others described it as “unreliable at the moment” (P17, Registrar). Shifts in the clinical environment also tempered expectations, staffing improved, and backlogs eased. By the postimplementation phase, perceptions of value became pragmatic and evidence-driven. Clinicians viewed the AI as a limited but occasionally useful decision support tool:

I’ve usually written my report before I look at this, and I don’t tend to change the report… it’s another look, I wouldn’t think of it as more than that
[P29, Consultant]

Concerns over cost-efficiency persisted:

If it was free, ambivalent… If it’s significant amounts of money… I don’t see the value because it’s more work than less.
[P29, Consultant]

Across phases, expectations regarding the AI shifted from broad hopes of efficiency to a more divided assessment. Some saw modest contributions to safety and prioritization, while others viewed the system as duplicating effort rather than providing genuine workload relief.

Supply-Side Value (Credibility and Clarity of Purpose)

At the same time, participants reflected on supply-side value, questioning how clearly the system’s purpose and evidence base had been articulated.

It just gives you pictures with circles. I’m not sure what the end use is meant to be.
[P20, Registrar]

By postimplementation, participants had a clearer understanding of what the AI could do but remained unconvinced of its overall value. However, there was also recognition that the AI was credible in concept but still immature in delivery.

It’s getting clearer now what it could be for, but it needs to evolve. Right now, it’s still just identifying, not interpreting over time.
[P43, Consultant]

Some viewed potential uses to optimize efficiency and workflow, with modifications:

It could identify which studies need to be reported first…or give us measurement readings.
[P46, Consultant]

Maybe some of those sorts of irritations around AI could be changed, you know, or fine-tuned.
[P25, Consultant]

These reflections indicated that perceptions of supply-side value were prospective, anchored in what the technology could deliver if optimized, rather than what it had yet achieved.

Across phases, the vendor narrative of innovation and efficiency had not yet translated into tangible or demonstrable benefit for clinicians or the wider health system. Participants viewed the AI as promising but still lacking the evidence and clarity needed to support confident investment or large-scale deployment.

Adopters

The NASSS adopter subdomain of role and identity is mapped with 2 inductive subthemes: professional positioning and negotiated use, and learning and preparedness. Together, these described how clinicians positioned AI within their expertise and accountability, and how limited exposure and training shaped trust, confidence, and uptake. Across phases, adoption reflected an oscillation between curiosity and skepticism, with trust becoming the key mediating factor.

Role and Identity (Professional Positioning and Negotiated Use)

At baseline, radiologists expressed a guarded willingness to engage with AI framed less as enthusiasm and more as a professional obligation.

I think I would use it…it would be almost negligent not to look at it.
[P6, Registrar]

Consultants saw potential for practical support:

It could certainly help you prioritize what you are watching and what order you report things.
[P8, Consultant]

During peri-implementation, practical experience unsettled this cautious trust. Registrars described false positives, excessive outputs, and low sensitivity:

It’s too junior at the moment.
[P17, Registrar]

Trust eroded not from resistance to innovation, but from inconsistency between the AI’s promise and its performance. Clinicians voiced a recurring sentiment that while AI might one day assist safety, it currently distracts from clinical focus:

If the volume of data presented is overwhelming, then that’s negative…the strength would be as a safety net for subtle findings, not changing an overall clinical picture.
[P19, Registrar]

Reflecting on their initial use of the tool during peri-implementation, a registrar noted:

“It was picking up stuff that wasn’t nodules…I still had to go back and look at the images again.
[P34, Registrar]

By postimplementation, there was selective use and partial trust.

I look through the nodules myself first and then correlate with the software to see whether it is congruent with what I’ve come up with.
[P42, Consultant]

Others disengaged entirely:

It slows you down because you have to verify each little dot.
[P37, Consultant]

A senior consultant likened the AI to “the registrar with clever ideas, but they’re all wrong” (P40, Consultant), useful for prompting review, yet unreliable without human correction.

Trust also intersected with medicolegal anxiety. Several raised uncertainties about accountability and liability:

If the software makes a mistake, who is liable—the vendor or the radiologist? We still haven’t ironed it out.
[P34, Registrar]

This uncertainty reinforced their instinct to retain manual control. As a consultant observed:

If I reported every possible little dot in the chest, I’d end up with a report ten pages long, which nobody would ever read.
[P37, Consultant]

The line between cautious trust and defensive practice remained thin.

Role and Identity (Learning and Preparedness)

Training and readiness remained persistently underdeveloped. During peri-implementation, there was no structured orientation or clear introduction to the system. Learning was largely self-directed and reliant on peer exchange.

Personally, I don’t think I’ve had any formal sit-down with it… I’ve just figured it out.
[P19, Registrar]

A vendor demonstration was held midphase, but not all clinicians attended, and some felt it was disconnected from practical workflow.

I just met the software without gathering any prior information about what this new software is.
[P34, Registrar]

Without clear instruction or transparency about performance parameters, early experiences became a process of trial and error rather than guided adoption, reinforcing skepticism instead of trust. By postimplementation, clinicians explicitly called for structured and continuous AI education embedded within clinical and professional frameworks.

If that is incorporated into our routine…every month we have our session doing AI cases.
[P32, Consultant]

Others stressed the need for broader institutional responsibility:

We are severely lacking in training with AI…it should be an integral, assessed part of our training program.
[P34, Registrar]

These calls reflected not only a desire for technical competence but also a wish to rebuild confidence and ensure medicolegal clarity, positioning AI as a tool that must be professionally standardized, not individually improvised.

Ultimately, clinicians saw AI competence as a new layer of professional literacy, necessary to protect judgment, maintain accountability, and engage critically with emerging tools. Their learning needs were not purely technical but ethical and epistemic: how to weigh evidence, interpret probability, and remain vigilant in an era of shared decision automation.

Wider System

Participants described the wider system as a persistent barrier across phases. This domain reflects the external political, policy, and institutional forces, such as regulation, professional guidance, legislative, and funding models that define the environment in which implementation occurs, but which local teams cannot directly control. While wider system themes were present across phases, they did not dominate every interview, and some subissues (for example, explicit references to legislation) appeared only sporadically.

At baseline, consultants depicted a public system tolerant of inefficiency and difficult to influence. Funding constraints were raised in the context of competing pressures.

Q-Health…there’s not much money around for these sorts of things.
[P1, Consultant]

During peri-implementation, registrars and consultants highlighted gaps in professional guidance and medicolegal expectations.

Training/communication…college says we need to learn AI, but little practical guidance…site-to-site differences.
[P24, Registrar]

Others wanted clearer, proactive communication from professional bodies.

College exposure is low.
[P27, Registrar]

Medicolegal norms were seen to expand review obligations when AI added extra views.

Mentality in radiology, if it’s on a screen, you have to comment on it, and medico-legal, if presented, we must review everything.
[P21, Consultant]

By postimplementation, some brought up broader ethical and policy concerns about data provenance and the social license concerns about using it:

There is concern about the way the data is being used…if all these algorithms are being trained on everyone’s data, it should be open source…it’s everyone’s.
[P42, Registrar]

Participants contrasted public and private system incentives and capacity, linking retention and deployment choices to wider economics and case-mix.

Public keeps me for complex cases, teaching, and feedback; private pays double.
[P28, Consultant]

Public vs private…pay and tech are better in private; public has collegiality and case mix.
[P42, Registrar]

Across phases, the wider system was characterized by limited policy levers and still developing structural and legislative readiness to support the rapid integration of AI into acute health care. Taken together, these wider-system influences interacted with internal organizational dynamics, staffing fluctuations, workload variability, and shifting operational priorities to shape the evolving trajectory of implementation across phases.

Discussion

Principal Findings

This study reports a prospective, qualitative, end-to-end evaluation of implementing an AI-driven clinical decision support system in a public radiology department, structured through the NASSS framework []. By mapping barriers and enablers across domains and phases, the study captures how early expectations shaped adoption, how sociotechnical challenges emerged during the rollout, and how these dynamics influenced long-term integration and adoption. Findings highlight that successful AI adoption depends not only on technical capability but on the alignment of organizational readiness, workflow design, and professional trust. Implementation success was governed by the interaction of multiple NASSS domains, which included interdependencies among technology, organization, adopters, and value rather than any single factor.

Weak planning and limited feedback structures (organizational barriers) amplified adopter frustrations with false positives, information clutter, and interoperability issues (technology barriers), eroding trust (adopter-level barriers). Even when technical faults were later mitigated, these initial experiences limited or constrained uptake, illustrating how early technical and communication failures created enduring impressions that shaped subsequent patterns of trust and tool use.

This mutual reinforcement of challenges across domains aligns with the complexity perspective described by Greenhalgh et al [] and Braithwaite et al [], whereby interacting barriers within and across a complex adaptive system, such as health care, tend to compound rather than resolve over time, particularly when they are not addressed in a coordinated and simultaneous manner. Clinicians’ perceptions of value were shaped primarily by how reliably and efficiently the AI system performed within everyday reporting workflows. Early false positives and excessive image sets undermined those expectations, diminishing confidence in the tool’s promised efficiency benefits. This finding is consistent with a recent qualitative study showing that workflow fit determines perceived usefulness, even for AI []. Participants described the system as “a check-off” tool rather than an integrated aid, and acceptance was suboptimal across our study; consistent with a 2023 semistructured interview with radiologists (n=25), which identified reliability, interpretability, and feedback transparency as decisive for AI acceptance []. Even after performance improved, initial mistrust persisted. This enduring skepticism also mirrors broader evidence that initial experiences set adoption trajectories and that trust is far easier to lose than to regain []. The finding reinforces the importance of consolidating the first-use experience through predeployment testing or “shadow mode” configurations [].

Organizational conditions were vital in shaping clinician engagement. During peri-implementation, fragmented communication and limited training left some staff unaware that the system had gone live, while others lacked the confidence to use it effectively. This mirrors the work of our team and others who have consistently highlighted that structured rollout, anticipatory planning, and capacity building are critical for sustainable digital adoption [,-]. Participants described the process as reactive and isolated rather than coordinated, reflecting the absence of a shared sense of purpose and mutual accountability between leadership, implementers, and users, necessary for effective implementation []. Although clinicians remained receptive to AI, they expected visible organizational commitment through ongoing education, rapid troubleshooting, and coherent leadership. In its absence, they relied on informal workarounds and peer support to conduct work-as-done, the adaptive, improvised practices that frontline staff develop to keep systems functioning when formal processes or resources fall short []. While such adaptations can sustain local functionality, they also introduce variability in care delivery and make it difficult to scale and standardize best practices across settings.

These organizational shortfalls also shaped how clinicians experienced implementation. In the absence of clear planning and coordination, several described feeling individually responsible for interpreting and integrating the AI tool into their workflow. This was not an explicit transfer of responsibility but reflected a professional culture in which clinicians relied on their own judgement to make the system workable within local constraints. Medicolegal uncertainty about liability further reinforced this guarded engagement. A 2021 narrative review observed that when accountability is unclear or diffuse, clinicians maintain human oversight to protect both patient safety and professional authority []. These dynamics highlight a core implementation challenge: without clear institutional responsibility and evidentiary assurance, professional caution becomes self-reinforcing, constraining experimentation, shared learning, and the normalization of AI within routine practice.

The single-site public hospital context influenced organizational capacity. Frequent staff rotations and high turnover meant that many clinicians engaged with the AI system intermittently, limiting opportunities for cumulative learning. Similar patterns are common across public health services, where workforce mobility and resource constraints make it difficult to sustain iterative improvement []. These dynamics interacted with the implementation process itself, shaping the pace and pattern of adoption and modulating how performance issues were perceived over time. Rather than functioning as external confounders, they formed part of the organizational ecology within which the AI system was introduced, influencing continuity, familiarity, and the stability of feedback loops essential for embedding new technologies. These conditions underscore the importance of implementation approaches that are designed for continuity, including modular onboarding, periodic refresher training, and accessible repositories of AI-related resources to support learning across changing teams.

This study extends the AI-in-radiology literature in 3 key ways. It adopts a temporal perspective, tracing implementation from early anticipation to postintegration and showing how initial optimism and early missteps shape later engagement. It also demonstrates that adoption was driven not by the innovation alone but by the interaction of technical performance, organizational coordination, and context, extending NASSS from description to explanation. In this framing, staff turnover, workload fluctuations, and shifting operational priorities function as active contextual determinants that help explain why implementation trajectories evolve as they do, rather than as background noise. Finally, through reflexive use of the framework, the study generates theoretical insight into why implementation evolves as it does, contributing to emerging work on domain interdependence and temporal complexity in health care innovation [].

Grounded in our findings and consistent with previous guidance [,,,], effective AI implementation in radiology depends on a combination of technical stability, communication, and organizational preparedness. Early “shadow mode” piloting helps identify faults and build trust before clinical use, while consistent communication about progress and fixes maintains transparency. Workflow-compatible design that minimizes cognitive load supports efficiency and acceptance []. Ongoing professional development, formal feedback channels with vendor responsiveness, and planning for workforce turnover through shared training repositories and local champions help sustain capability over time. Together, these strategies align with the six principles of FUTURE-AI recommendations encompassing fairness, universality, traceability, usability, robustness, and explainability, an international expert-driven consensus to facilitate the adoption of trustworthy medical imaging [] and emphasize that co-design and iterative learning are essential to long-term adoption.

Study Limitations and Strengths

This was a single-site study, and further real-world, ecological research is needed to identify determinants of adoption and create solutions that generalize across health systems. Despite this, our findings are similar to several recently published studies examining radiologists’ perceptions around the adoption of AI into standard practice [,,,-]. There was low uptake of the tool during the peri-implementation period due to technical challenges. This limited wider evaluation of this crucial implementation period, particularly in terms of clinical usefulness. The 18-month period may have also introduced a range of system confounders, including staffing changes and organizational priorities, which may have impacted how participants felt about the AI clinical decision support tool. Finally, social desirability bias cannot be ruled out, particularly as this was a department-wide implementation. However, this was mitigated through participant briefings emphasizing the exploratory nature of the trial, coupled with reflexive practice by the interview team []. A key study strength was that this was a real-world evaluation demonstrating ecological validity with findings grounded in practical realities. Second, aligning our findings with a validated implementation science framework supports theoretical transferability and future application in related contexts despite the single-site limitations.

Future Implications

Future studies should integrate qualitative and quantitative data, combining workflow observations with metrics such as reporting time, error rates, and AI usage logs, to triangulate findings. Multisite evaluations across differing levels of digital maturity are needed to test transferability and examine how governance, culture, and workforce patterns influence scalability.

Conclusion

Implementation of AI-based decision support in radiology is as much an organizational and cultural process as a technological one. Clinicians remain willing to engage, but sustainable adoption depends on consolidating early experiences, embedding communication and training, and maintaining iterative feedback between users, vendors, and system leaders. Applying the NASSS framework revealed how domains interact dynamically across time, offering both theoretical insight into sociotechnical complexity and practical guidance for hospitals seeking to move from pilot to routine, trustworthy AI integration.

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Introduction

Background

When people look for health information today, they no longer only consult physicians, pharmacists, or search engines. Increasingly, they also encounter generative artificial intelligence (AI) tools such as ChatGPT or the World Health Organization (WHO)’s chatbot Sarah, which simulate human-like conversations and provide instant responses. These tools promise a new way of accessing medical knowledge: fast, convenient, and interactive. At first glance, this accessibility seems to hold great potential for reducing barriers to health information, therefore directly impacting digital health equity—defined as equitable access to and use of digital health information technology that supports informed decision-making and enhances health [].

However, the picture is more complex. On the one hand, generative AI can offer cost-free entry points (eg, basic versions of ChatGPT or automatically displayed answers in Google search via Google’s Gemini), deliver content in multiple languages, and rephrase complex medical concepts into more understandable terms. In doing so, it could strengthen patient education, address health inequalities, and help bridge communication gaps between citizens and health care providers [,]. On the other hand, effective use still depends on internet-enabled devices and adequate digital skills, which are not equally distributed. As a result, the very technology that appears open and inclusive may also risk exacerbating existing digital divides [].

Moreover, unlike other types of information, health-related questions are often sensitive and personal. At the same time, the inner workings of generative AI remain opaque, and the accuracy of its outputs is not guaranteed []. All these tensions raise important questions about adoption: Who is most likely to turn to generative AI for health information, and what factors shape this intention? Moreover, since health communication practices and digital infrastructures differ across countries, cross-national research is urgently needed.

To address these questions, this study draws on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) []. The model proposes that performance expectancy, effort expectancy, facilitating conditions, social influence, habit, and hedonic motivation shape technology use. We extend this framework by also examining the roles of health literacy and health status in predicting intention to use generative AI for health information. Using cross-national survey data from Austria, Denmark, France, and Serbia, we investigate the drivers of adoption to shed light on both individual and contextual factors that may guide the diffusion of generative AI in health contexts.

Generative AI as Novel Health Information Source

Generative AI constitutes a potentially disruptive force in the health information ecosystem []. However, despite its rapid advancement and widespread availability, empirical research on its role in health information–seeking remains limited []. At the same time, broader trends highlight the ongoing digitalization of health-related knowledge acquisition. A representative survey conducted in Germany in 2019 revealed that only 48% of respondents consulted a medical professional for their most recent health issue, while 1 in 3 turned first to the internet []. Similar findings show that online sources—particularly search engines—are the primary means of accessing health information, both for caregivers and the general population [,]. Family and friends and traditional mass media (eg, print media and health-related TV programming) rank behind medical professionals and online sources [].

The introduction of generative AI tools like ChatGPT may shift these established hierarchies. Unlike static web content or conventional search engines, generative AI enables dialogic, personalized interactions that simulate human conversation. These features may position generative AI as a compelling alternative to established online and offline health information sources. However, current evidence suggests that trust in generative AI—especially regarding complex health-related issues—is still limited [], which might restrict its present adoption potential to early adopters []. This raises questions about how generative AI integrates into the broader ecosystem of health information sources.

To address this gap, we first explore: How does the use of generative AI for health information–seeking compare to that of more established health information sources?

Explaining Predictors of Technology Adoption: UTAUT2

The UTAUT2 [] is one of the most popular models to explain technology adoption. It builds on the technology acceptance model [], emphasizing perceived usefulness and ease of use, and the initial UTAUT model [], which added performance expectancy, effort expectancy, facilitating conditions, and social influence as predictors of adoption behavior. UTAUT2 extends these frameworks to consumer contexts by incorporating hedonic motivation and habit [,] . The UTAUT2 model has demonstrated its versatility in explaining the adoption of diverse eHealth technologies, such as wearable devices [], health websites [], and health apps []. Additionally, recent studies have highlighted its relevance in understanding the uptake of generative AI technologies [-], showcasing its capacity to extend beyond traditional eHealth domains. However, so far, studies on predictors of usage intentions in the context of AI health information–seeking are lacking.

Performance expectancy, a central construct in the UTAUT2 framework, reflects the belief that using technology will lead to performance benefits []. In the context of health information–seeking using generative AI, performance expectancy is shaped by users’ perceptions of how effectively these tools can enhance their own life, including aspects such as health–decision-making and task efficiency []. Consequently, as users anticipate greater usefulness from adopting generative AI as a health information source, their intention to use such technologies strengthens [-,]. Based on this, we propose the following hypothesis: “the higher the performance expectancy, the stronger the intention to use generative AI for health information–seeking” (H1). Effort expectancy, closely tied to ease of use, emphasizes simplicity in technology adoption []. Generative AI tools like ChatGPT benefit from high effort expectancy when users find them intuitive and easy to integrate into their workflows, particularly during the early adoption phase [,,,]. Addressing usability concerns early can reduce resistance and build user confidence, strengthening behavioral intention [,]. Therefore, we propose that “the higher the effort expectancy, the stronger the intention to use generative AI for health information–seeking” (H2). Facilitating conditions refer to the resources, skills, and support necessary for using technology []. These include training, knowledge, technical assistance, and system compatibility, which significantly enhance behavioral intention and usage [,]. In technologically mature settings, facilitating conditions are critical for sustained adoption and user satisfaction []. In line with the UTAUT2, we hypothesize that “the better the facilitating conditions, the stronger the intention to use generative AI for health information–seeking” (H3). Social influence indicates the perception that peers, such as family, friends, or colleagues, believe one should adopt a technology []. It plays a crucial role in early adoption, where external validation often outweighs personal experience []. Positive reinforcement within social or professional networks can normalize usage [,,,]: If people perceive that their peers already use generative AI for health information–seeking, their own intention to do so might increase as well. We, therefore, propose that “the greater the perceived social influence, the stronger the intention to use generative AI for health information–seeking” (H4). Habit specifies the extent to which behavior becomes automatic through repetition and prior use []. It strongly influences behavioral intention and long-term adoption, emphasizing the importance of regular engagement with technology [,]. For generative AI as a health information source, fostering habitual use can solidify its integration into daily routines and enhance sustained adoption []. This leads us to state, “the more it is a habit to use generative AI, the stronger the intention to use generative AI for health information–seeking” (H5). Hedonic motivation refers to the enjoyment or pleasure derived from using technology, particularly relevant in consumer contexts []. It directly impacts behavioral intention, especially for technologies involving entertainment or leisure []. For generative AI like ChatGPT, it can be expected that the interaction is perceived as fun or entertaining, which can boost user engagement and drive adoption []. Accordingly, we suggest the following hypothesis: “the higher the hedonic motivation, the stronger the intention to use generative AI for health information–seeking” (H6).

Influence of Health Literacy and Health Status

With the growing integration of digital tools into everyday lives, the role of health literacy in online health information–seeking has garnered increasing attention. Health literacy has been conceptualized as an individual’s capacity to search, access, comprehend, and critically evaluate health information, as well as to use the acquired knowledge to effectively address health-related issues [,]. Digital health literacy refers to these abilities in the context of digital environments [-]. Generally, low health literacy scores have been associated with undesirable health outcomes [].

Research suggests that low levels of health literacy are associated with decreased trust in online health resources [], including the outputs of AI tools [], and lower overall adoption of online health technologies []. Furthermore, initial studies indicate a positive association between health literacy levels and attitudes toward the use of AI tools for medical consultations [].

On the other hand, individuals with higher levels of health literacy are generally better equipped to critically evaluate online health information and scrutinize it in greater detail [,]. This heightened evaluative capacity could make them more aware of the limitations and potential risks of generative AI outputs, such as inaccurate information, bias, data privacy concerns, or oversimplified medical advice []. Moreover, individuals with higher health literacy are more likely to trust and use high-quality medical online resources, whereas those with limited health literacy prefer accessible but potentially less reliable sources []. In this context, outputs from generative AI might be perceived as lower-quality sources by highly digital health–literate individuals. As a result, while higher health literacy could foster openness to using generative AI for health purposes, it might also lead to greater skepticism or hesitancy in relying on these tools. Nonetheless, there is not enough research in the context of generative AI specifically to make conclusive predictions.

Another well-established factor in online health information–seeking, yet underexplored in the context of AI, is individuals’ health status: Studies suggest that people with poor health are significantly more likely to consult the internet for health information compared to those with good health [,]. Being chronically ill has also been associated with increased reliance on internet-based technologies for health-related purposes []. This relationship can be explained by the fact that individuals in poor health often experience greater health-related concerns, which in turn heightens their motivation to seek information online.

Given these complex relationships, we propose the second research question: How does health literacy and health status influence the intention to seek health information using generative AI?

Cross-National Comparison

In this study, we investigate the predictors of generative AI adoption for health information–seeking across 4 European countries: Austria, Denmark, France, and Serbia. While these countries share certain similarities, they also display notable differences that could shape the strength of the UTAUT2 predictors on the intention to use generative AI for health purposes. Thus, this cross-national approach ensures that the observed effects are generalizable and not confined to specific national contexts or unique country conditions.

The selected countries share two key characteristics. First, all 4 countries provide universal health coverage, ensuring broad access to health care services for their populations. Second, a significant portion of health care expenditure in these countries is publicly funded [-].

Despite these commonalities, there are also critical factors that differ among the countries and may shape the predictors of generative AI adoption. On the one hand, variations in digital infrastructure could significantly impact facilitating conditions, effort expectancy, and social influence as predictors of generative AI use. Denmark consistently ranks among Europe’s most digitally advanced nations, boasting high internet penetration and widespread adoption of e-health solutions []. This strong digital ecosystem likely enhances the perceived ease of use and social endorsement of generative AI. In contrast, Austria, France, and Serbia exhibit more moderate levels of digital adoption in the context of health information, which may limit the perceived use and social norms regarding such technologies [].

On the other hand, access to and trust in health care providers vary significantly across these countries, potentially influencing performance expectancy and social influence. In nations with robust health care systems—characterized by a high availability of medical professionals and easy access to care—individuals are more likely to rely on doctors for health advice, as they are often viewed as the most trusted source of health information []. Denmark exemplifies this with its high levels of public trust in the health care system [], which may reduce the perceived benefits and social norms around using generative AI for health purposes. Conversely, in western Balkan countries like Serbia, studies report generally low levels of trust in the health care system []. In such contexts, individuals may be more inclined to seek alternative information sources, potentially amplifying the perceived benefits of generative AI use.

By examining these diverse national contexts, this study not only tests the universality of the UTAUT2 model but also deepens our understanding of the contextual factors that shape generative AI adoption for health purposes. We ask: How do the predictors of generative AI use for health information–seeking differ across Austria, Denmark, France, and Serbia?

Methods

Ethical Considerations

Before data collection, the study received ethical approval from the institutional review board of the Department of Communication at the University of Vienna (approval ID: 1205). All participants provided written informed consent prior to participating in the study. The data were collected in anonymized form and no personal identifiers were recorded or stored. Participants received a compensation of €1.50 (US $1.74) for completing the study through the panel provider.

Recruitment

Recruitment of participants occurred during September 2024 via Bilendi, an international panel provider. Bilendi recruited the participants via email. The panel is checked for quality and attendance on a regular basis. The study was conducted in Austria, France, Denmark, and Serbia, with participants randomly selected to achieve samples representative of age, gender, and educational background. The provider’s panel sizes in the respective countries were as follows: Austria: n=60,000; Denmark: n=90,000; France: n=815,000; and Serbia: n=15,100. Per country, the study aimed to reach 500 participants.

Inclusion criteria required participants to be aged between 16 and 74 years. Additionally, respondents who completed the survey in less than one-third of the median completion time (speeders) were excluded. Completion rates (excluding screened-out participants) were high across all 4 countries, ranging from 84.3% to 89.8%. Further details on survey design, administration, and response rates are provided in the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) checklist ().

Procedure and Measures

Overview

The study consisted of two components. The first component, a survey, investigated predictors of the intention to use generative AI for health information–seeking. The second component, an experimental study, explored the influence of disease-related factors on these intentions []. To ensure respondents shared a common understanding of the concept, the survey began with a short definition of “generative AI,” describing it as technologies that engage in natural language conversations with users and generate responses in real-time. Examples such as ChatGPT, Google’s Gemini, and Microsoft Copilot were provided.

The original questionnaire was developed in English and subsequently translated into German, French, Danish, and Serbian. Each translation was performed by a bilingual team member and back-translated into English by a different native speaker to ensure conceptual equivalence with the original items.

This study focused on the following constructs (a complete item list with descriptive analysis and construct reliability values can be found in ).

Dependent Variables

Sources of Health Information–Seeking

Participants rated the frequency with which they use 8 health information [] sources on a 7-point Likert scale (1 = “never” to 7 = “very often”). The sources included conversations with medical professionals, pharmacists, and family or friends, as well as books, mass media, internet search engines (eg, Google and Ecosia), and generative AI.

AI Usage Intentions

Participants’ willingness to use generative AI [] for health information–seeking was assessed using 3 items (eg, “I intend to use generative AI for health information seeking”), rated on a 7-point Likert scale (1 = “strongly disagree” to 7 = “strongly agree”).

UTAUT2 Predictor Variables

All UTAUT2 model [,,,] predictors were measured on a 7-point Likert scale (1 = “strongly disagree” to 7 = “strongly agree”). The predictors have been described below.

Performance Expectancy

Perceived benefits of using generative AI for health information–seeking were measured with 4 items (eg, “Using generative AI would save me time when researching health topics”).

Effort Expectancy

The perceived ease of using generative AI as a health information source was assessed with 4 items (eg, “Learning to use generative AI for health information–seeking seems easy for me”).

Social Influence

Three items measured the extent to which participants felt that important others encouraged their use of generative AI for health information–seeking (eg, “People who are important to me think that I should use generative AI for health-information seeking”).

Hedonic Motivation

The enjoyment of using generative AI for health information–seeking was assessed with 3 items (eg, “I think using generative AI for health-information seeking could be fun”).

Facilitating Conditions

Participants’ perceptions of available resources and support for using generative AI to seek health information were measured with 4 items (eg, access to devices and reliable internet, and knowledge).

Habit

The extent to which turning to generative AI when seeking health information had become a habitual behavior was measured with 3 items (eg, “I automatically turn to generative AI whenever I have questions about my health”).

Model Extension Variables

Health Literacy

Health literacy [] was assessed with 10 items, asking participants to rate their confidence in tasks such as finding understandable health information. Responses were recorded on a 4-point Likert scale (1 = “not at all true” to 4 = “absolutely true”).

Health Status

We measured participants’ health status using 1 item (“How would you describe your current health status?”; 1 = “very poor” to 7 = “very good”).

Control Variables: Sociodemographic Variables

We further measured participants’ age, gender, and educational level.

Statistical Analysis

Power

An a priori power analysis was conducted to determine the required sample size for structural equation modeling. Assuming an anticipated effect size of 0.25, a desired statistical power of 0.95, and a significance level of .05, the analysis indicated that a minimum of 391 participants per country would be necessary to detect the hypothesized effects [].

Analytical Plan

We used AMOS version 26 (IBM Corp) to run a latent variable, multigroup structural equation model using a maximum-likelihood estimator with full information. We computed the comparative fit index, the Tucker-Lewis Index, the chi-square to degrees of freedom ratio (χ²/df), and the root mean square error of approximation. We also secured metric measurement invariance to be able to compare the paths across countries. We controlled for age, gender, and education (binary coded).

Results

User Statistics

In total, data were collected from 1990 respondents, comprising 502 from Austria, 507 from Denmark, 498 from France, and 483 from Serbia. The overall mean age of participants was 45.1 (SD 15.7) years, with 50.2% (n=998) identifying as female participants. In terms of educational attainment, 83.8% (n=1634) of the sample reported completing at least a medium or higher level of education (secondary level II or higher). Furthermore, 87.4% (n=787) of respondents indicated prior use of generative AI for health information–seeking (at least rarely). Detailed demographic and background characteristics of the sample are summarized in .

Statistical tests revealed no significant differences in gender distribution across countries (χ²₃=0.48; P=.92) and no significant differences in age (Kruskal-Wallis χ²₃=2.15; P=.54). In contrast, educational attainment varied significantly between countries (χ²₃=550.76; P<.001), reflecting sampling-related imbalances in Serbia where low versus medium or high education was assessed differently than in the other countries. Finally, prior experience with health information–seeking showed significant country differences (Kruskal-Wallis χ²₃=30.95; P<.001), with higher levels reported in Serbia.

Evaluation Outcomes

Descriptive Analysis

In our first research question, we explored how generative AI compares to more established health information sources in terms of usage frequency across countries. As illustrated in , generative AI ranks last among all measured sources, indicating that, as of autumn 2024, it is rarely used for health information–seeking (mean 2.08, SD 1.66). In stark contrast, online search engines like Google are highly used, ranking second with a mean usage frequency of 4.57 (SD 1.88), following conversations with physicians, which hold the top position (4.77, SD 1.70). Family and friends also play a significant role, ranking third (4.27, SD 1.73), alongside pharmacists (3.52, SD 1.81). In comparison, traditional mass media such as TV, newspapers, and magazines are used less frequently (2.74, SD 1.68), as are books (2.68, SD 1.70) and free magazines provided by pharmacies or health insurance companies (2.60, SD 1.65). The relative ranking of information sources was consistent across all 4 countries, with physicians, internet search engines, and family or friends occupying the top positions and generative AI ranking last. However, some variation in mean usage frequencies was observed between countries; detailed country-level results are presented in .

‎

Model Evaluation

For the hypothesis tests, the results are shown in . Model fit was good (comparative fit index=0.95; Tucker-Lewis-Index=.93; χ²/df =2.47, P<.001; root mean square error of approximation=0.03, 95% CI 0.03-0.03). We examined the metric measurement invariance of all latent variables by constraining all factor loadings as equal for all 4 countries. When comparing the constrained model to the unconstrained model, we found no significant difference in model fit (P=.16). Thus, metric invariance across countries was established.

In line with H1, we found a highly significant positive association between performance expectancy and the intention to use generative AI for health information–seeking across all 4 countries (Austria: b=0.47, P<.001; Denmark: b=0.52, P<.001; France: b=0.53, P<.001; Serbia: b=0.44, P<.001). In contrast, H2 was not supported: effort expectancy showed no significant association with behavioral intention in any of the countries. Turning to H3, results revealed a positive association between facilitating conditions and the intention to use generative AI as a health information source, consistently observed across all 4 contexts (Austria: b=0.12, P=.005; Denmark: b=0.17, P<.001; France: b=0.22, P<.001; Serbia: b=0.24, P<.001). By contrast, no support was found for H4: perceived social influence was unrelated to behavioral intention in any of the countries. As predicted in H5, habit was positively associated with behavioral intention to use generative AI for health information–seeking throughout the sample (Austria: b=0.29, P<.001; Denmark: b=0.32, P<.001; France: b=0.28, P<.001; Serbia: b=0.28, P<.001). A similar pattern emerged for H6: hedonic motivation was significantly positively related to behavioral intention in all countries (Austria: b=0.45, P<.001; Denmark: b=0.22, P<.001; France: b=0.33, P<.001; Serbia: b=0.23, P<.001).

Finally, with regard to our second research question—which examined whether health literacy and health status predict the intention to seek health information using generative AI—we found no substantial associations. Only in France did health status show a marginal negative effect (b=−0.09; P=.007).

Discussion

Principal Results

This study investigated the predictors of intention to use generative AI for health information–seeking, drawing on the UTAUT2 framework and expanding it with health literacy and health status. Using cross-national survey data from Austria, Denmark, France, and Serbia, our findings show that generative AI is still only rarely used for health information–seeking. At the same time, performance expectancy, facilitating conditions, habit, and hedonic motivation consistently emerged as significant predictors of behavioral intention, whereas effort expectancy, social influence, health literacy, and health status were not related to intention. These patterns were consistent across all 4 countries, suggesting a robust set of psychological drivers underlying the early adoption of generative AI in health contexts. A detailed examination of these findings is provided as follows.

First, with regard to overall usage patterns, the data shows that generative AI currently plays only a minor role in health information–seeking: 60% of the respondents reported never having used a generative AI tool for health-related questions. This result lends itself to 2 contrasting interpretations.

On the one hand, it challenges the popular narrative that generative AI is rapidly transforming health information–seeking behavior. Instead, the findings align with previous studies, showing that generative AI is currently infrequently used in the context of health information []. Traditional sources—such as medical professionals and search engines—continue to dominate [], underscoring that generative AI has yet to achieve mainstream adoption.

On the other hand, despite persistent concerns about data privacy, algorithmic bias, and accuracy, it is noteworthy that 40% of the respondents have already experimented with generative AI for health purposes. Given that this technology only became widely accessible relatively recently, such early uptake is remarkable. From the perspective of technology adoption models, such as the Rogers Diffusion of Innovations [], this pattern is characteristic of early adopters. It is therefore plausible to assume that the use of generative AI for health information–seeking will increase further as the technology matures and moves toward mainstream adoption.

To better understand the drivers of future uptake, we applied an extended version of the UTAUT2 model. Our findings confirmed the predictive power of performance expectancy, facilitating conditions, habit, and hedonic motivation. This aligns with prior research on digital health tools, indicating that users value usefulness, access, familiarity, and enjoyment [,,].

In detail, the results show that performance expectancy—the perceived usefulness of the technology—had a strong positive effect on behavioral intention in all four countries. This finding suggests that the more respondents believe generative AI is useful to manage health-related questions, the more they will use it. Thus, if public health stakeholders or developers aim to encourage responsible AI use, they should emphasize the tangible benefits of generative AI, such as 24/7 availability, rapid response times, and the potential for personalized information. Perceived usefulness may also be fostered when individuals try out generative AI for the first time, that is, they learn that they can benefit from the technology.

At the same time, our study challenges established UTAUT2 assumptions. Effort expectancy, often seen as central to technology adoption, was not a relevant factor—possibly due to the intuitive nature of generative AI tools and the ubiquity of basic digital skills []. Using generative AI does not require any specific background knowledge beyond opening a webpage. Since online search engines are already the most frequently used health source, the basic skills needed for generative AI are widely present, potentially rendering effort expectancy less decisive.

Taken together, this emerging pattern—the strong effect of performance expectancy and the null effect of effort expectancy—underscores the distinction between usefulness and usability, which are closely related but not identical []. Usability refers to the ease of interacting with a system (eg, ease of learning and error prevention), whereas usefulness (utility) captures whether the system provides the functions and information that users actually need. Our findings suggest that in health contexts, utility is the decisive factor: people intend to use generative AI if its outputs are perceived as useful, while usability-related aspects appear less influential.

Importantly, this does not mean that barriers to adoption are absent. Rather, our findings show that they lie not in usability but in facilitating conditions—the structural and contextual resources that enable technology use. Across all countries, the availability of digital infrastructure, devices, and basic knowledge significantly shaped behavioral intention. In other words, while generative AI may be easy to use once accessed, unequal access to the necessary resources continues to pose a substantial adoption barrier. Consequently, facilitating conditions emerge as a key digital health equity concern []. Without adequate access, disadvantaged populations may be excluded from benefiting from generative AI, meaning that the technology risks widening rather than narrowing the digital divide in health information–seeking.

We also found that social influence—an important predictor in other studies on AI uptake [,]—did not play a meaningful role in shaping behavioral intention. This suggests that health-related information search is a rather personal topic, and that individuals may not always be willing to disclose what kind of information they are looking for. As a result, the intention to use generative AI for health information–seeking is largely independent of peer opinions or social norms.

In contrast, habit consistently predicted behavioral intention across all countries. From this finding, we may conclude that generative AI use for health information is likely to occur automatically, similar to how people use search engines. When individuals feel familiar with a technology, they are more likely to rely on it without conscious deliberation. However, this finding should be interpreted with caution, as the majority of participants had never used generative AI for health purposes. Much of the variance in habit may therefore reflect mere use versus nonuse. Accordingly, variables capturing initial adoption should be clearly distinguished from those measuring habit.

By including health literacy and health status as additional predictors, our study adds a novel dimension to existing research. In contrast to studies showing direct paths between these constructs and online health information–seeking [,,], we found no such association for AI health information–seeking. However, each of these findings carries different implications. First, the absence of a significant association between health literacy and intention indicates that individuals’ ability to understand and evaluate health information was not related to whether they reported turning to generative AI. This finding may suggest that the use of such tools is driven less by informed decision-making and more by general curiosity or interest in new technologies. Importantly, this raises concerns: people with lower health literacy may be just as likely to consult generative AI as those with higher health literacy—despite being less equipped to critically assess its outputs. Given the known risk of AI hallucinations—fabricated or inaccurate information presented in a confident tone []—this could lead to misinformation and, in the worst case, harmful health decisions, as users with limited health literacy might find it difficult to distinguish between reliable and misleading content.

Second, the lack of an association between self-reported health status and intention suggests that the current use of generative AI is not primarily driven by medical need or urgency. People do not seem more likely to consult generative AI when facing a health problem; rather, usage may occur proactively or even recreationally. This challenges assumptions that such tools are primarily used in response to a health issue, and it underscores the importance of understanding user motivations beyond immediate health concerns.

Importantly, these patterns were largely consistent across all 4 countries, as confirmed by the measurement-invariant structural model. This cross-national consistency suggests that the psychological drivers of generative AI adoption in health contexts may transcend national boundaries and cultural differences, pointing to a universal set of adoption mechanisms.

Limitations

Several limitations should be acknowledged. First, due to the cross-sectional nature of this study, no causal conclusions can be drawn. Future research should therefore aim to replicate these findings using experimental or longitudinal designs. Second, we relied on self-reported data, which may be subject to social desirability bias. The use of behavioral data is thus warranted to validate these findings. Third, including additional predictors—such as individual differences or specific concerns—could provide deeper insights into the use of generative AI. Fourth, our comparative findings are based on data from only 4 countries, which limits the ability to conduct multilevel analyses. Also, as in all cross-sectional research, there is a risk of unmeasured third variables. In particular, we did not include AI trust and perceived AI risk. However, these constructs are conceptually close to performance expectancy, as trust reduces uncertainty about the system’s outputs and thereby enhances expected performance gains, whereas perceived risk erodes expected utility. In this sense, they are likely to be partially reflected in the performance expectancy construct already included in our model. That said, and highlighting that our model explains around 80% of the variance, trust and perceived risk could still suppress some of the predictors we have modeled. Thus, future research should include additional constructs outside the UTAUT2 framework []. Finally, health status was measured with a single self-rated item. While single-item measures of subjective health may not capture the full complexity of an individual’s medical condition, this approach is widely used in demographic and population health research. Prior work has demonstrated that the self-rated health item is a valid and reliable indicator, predicting key outcomes such as mortality, use of health services, and health expenditures in large-scale surveys []. Nevertheless, we acknowledge that a more fine-grained measure (eg, including specific chronic conditions or severity indices) could have provided additional insights, and future studies may benefit from applying such extended health measures.

Conclusions

This study applied the UTAUT2 model to investigate the factors that drive the use of generative AI for health information–seeking. Although overall usage remains limited, our findings show that performance expectancy, facilitating conditions, habit, and hedonic motivation are positively associated with behavioral intentions. These patterns, observed across all 4 countries—Austria, Denmark, France, and Serbia—suggest that current users of generative AI are likely to be early adopters: individuals who are tech-savvy, curious, and open to innovation. This aligns with the Rogers Diffusion of Innovations theory, which conceptualizes adoption as a gradual process beginning with a small, innovation-oriented segment of the population.

The lack of significant effects for effort expectancy and social influence across all countries reinforces this interpretation: early adopters tend to base their decisions on personal evaluations rather than external opinions and are rarely deterred by usability concerns. Furthermore, the fact that behavioral intention was unrelated to health status or health literacy underscores that current usage is not driven by acute medical need or advanced health literacy, but rather by interest, convenience, and technological exploration.

The cross-national consistency of these findings is particularly striking. Despite differences in health care systems, digital infrastructures, and culture, the same psychological and contextual factors influenced generative AI use in all countries surveyed. This suggests a shared adoption logic that transcends national boundaries—at least in the early stages of diffusion.

Looking ahead, these insights help illuminate how generative AI might transition from a niche tool to a widely used resource. As the technology becomes more embedded in everyday life, broader segments of the population—the so-called early and late majority—will likely demand stronger assurances of trustworthiness, safety, and added value. To enable responsible and inclusive adoption, it is therefore crucial to reduce digital access barriers, enhance transparency, and implement safeguards against health misinformation, especially for users with limited health literacy.

From a practical perspective, our findings suggest that communication strategies aiming to promote generative AI for health purposes should emphasize concrete benefits, ease of access, and even enjoyment. Rather than exclusively targeting individuals with chronic or urgent health needs, positioning generative AI as an engaging, low-barrier tool may broaden its appeal—reaching users who might otherwise be disengaged from traditional health information sources.

In sum, generative AI holds significant potential as a future health information resource—but its trajectory will depend on how well we understand and support the evolving needs of its users across different adoption phases and contexts.