Determinants of health. https://www.who.int/news-room/questions-and-answers/item/determinants-of-health#:~:text=The%20determinants%20of%20health%20include,person’s%20individual%20characteristics%20and%20behaviours (2024).
Wakefield, M. K., Williams, D. R., Le Menestrel, S. & Lalitha, J. The Future of Nursing 2020–2030: Charting a Path to Achieve Health Equity (National Academies Press, 2021).
Mendelsohn, A. B. et al. Characterization of missing data in clinical registry studies. Ther. Innov. Regul. Sci. 49, 146–154 (2015).
Google Scholar
Gliklich, R. E., Leavy, M. B. & Dreyer, N. A. Tools and technologies for registry interoperability, registries for evaluating patient outcomes: a user’s guide, addendum 2. Agency for Healthcare Research and Quality (2019).
Fernainy, P. et al. BMC Proceedings (Springer).
Kandi, V. & Vadakedath, S. Clinical trials and clinical research: a comprehensive review. Cureus 15, e35077 (2023).
Averitt, A. J., Ryan, P. B., Weng, C. & Perotte, A. A conceptual framework for external validity. J. Biomed. Inform. 121, 103870 (2021).
Google Scholar
Ytterberg, S. R. et al. Cardiovascular and cancer risk with tofacitinib in rheumatoid arthritis. N. Engl. J. Med. 386, 316–326 (2022).
Google Scholar
Lee, D. et al. genrct: a statistical analysis framework for generalizing RCT findings to real-world population. J. Biopharm. Stat. 34, 873–892 (2024).
Google Scholar
Hernandez, I., Baik, S. H., Piñera, A. & Zhang, Y. Risk of bleeding with dabigatran in atrial fibrillation. JAMA Intern. Med. 175, 18–24 (2015).
Google Scholar
Alarcón Garavito, G. A. et al. Enablers and barriers of clinical trial participation in adult patients from minority ethnic groups: a systematic review. Trials 26, 65 (2025).
Google Scholar
Getz, K. How much does a day of delay in a clinical trial really cost? Appl. Clin. Trials 33 (2024).
Chandra, S., Prakash, P., Samanta, S. & Chilukuri, S. ClinicalGAN: powering patient monitoring in clinical trials with patient digital twins. Sci. Rep. 14, 12236 (2024).
Google Scholar
Lamberti, J. inHEART initiates randomized controlled trial for its AI-enabled digital twin of the heart. https://www.inheartmedical.com/news/inheart-initiates-randomized-controlled-trial-for-its-ai-enabled-digital-twin-of-the-heart (2025).
Matzenbacher, L. S. et al. Interactive virtual assistant for health promotion and diabetes care in older adults with diabetes—a randomized controlled trial. Diabetes 74, 297-OR (2025).
Google Scholar
Lam, T. Y. et al. Randomized controlled trials of artificial intelligence in clinical practice: systematic review. J. Med. Internet Res. 24, e37188 (2022).
Google Scholar
Akbarialiabad, H. et al. Bridging silicon and carbon worlds with digital twins and on-chip systems in drug discovery. npj Syst. Biol. Appl. 10, 150 (2024).
Google Scholar
Tao, F. & Qi, Q. Make more digital twins. Nature 573, 490–491 (2019).
Google Scholar
Wang, G. et al. Development of metaverse for intelligent healthcare. Nat. Mach. Intell. 4, 922–929 (2022).
Google Scholar
Voigt, I. et al. Digital twins for multiple sclerosis. Front. Immunol. 12, 669811 (2021).
Google Scholar
Wu, C. et al. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. Biophys. Rev. 3, 021304 (2022).
Grieb, N. et al. A digital twin model for evidence-based clinical decision support in multiple myeloma treatment. Front. Digit. Health 5, 1324453 (2023).
Google Scholar
Vidovszky, A. A. et al. Increasing acceptance of AI-generated digital twins through clinical trial applications. Clin. Transl. Sci. 17, e13897 (2024).
Google Scholar
Ghaffar Nia, N., Kaplanoglu, E. & Nasab, A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discov. Artif. Intell. 3, 5 (2023).
Google Scholar
Croatti, A., Gabellini, M., Montagna, S. & Ricci, A. On the integration of agents and digital twins in healthcare. J. Med. Syst. 44, 161 (2020).
Google Scholar
Ross, J. L., Sabbaghi, A., Zhuang, R., Bertolini, D. & Initiative, A. s. D. N. Enhancing longitudinal clinical trial efficiency with digital twins and prognostic covariate-adjusted mixed models for repeated measures (PROCOVA-MMRM). Preprint at arXiv:2404.17576 (2024).
Schwartz, S. M., Wildenhaus, K., Bucher, A. & Byrd, B. Digital twins and the emerging science of self: implications for digital health experience design and “small” data. Front. Comput. Sci. 2, 31 (2020).
Google Scholar
Jung, A., Gsell, M. A., Augustin, C. M. & Plank, G. An integrated workflow for building digital twins of cardiac electromechanics—a multi-fidelity approach for personalising active mechanics. Mathematics 10, 823 (2022).
Google Scholar
Barat, S. et al. An agent-based digital twin for exploring localized non-pharmaceutical interventions to control covid-19 pandemic. Trans. Indian Natl Acad. Eng. 6, 323–353 (2021).
Google Scholar
Martinez-Velazquez, R., Gamez, R. & El Saddik, A. In 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 1–6 (IEEE, 2019).
Joslyn, L. R., Huang, W., Miles, D., Hosseini, I. & Ramanujan, S. Digital twins elucidate critical role of Tscm in clinical persistence of TCR-engineered cell therapy. NPJ Syst. Biol. Appl. 10, 11 (2024).
Google Scholar
Sinisi, S. et al. Optimal personalised treatment computation through in silico clinical trials on patient digital twins. Fundam. Inform. 174, 283–310 (2020).
Google Scholar
Inan, O. T. et al. Digitizing clinical trials. NPJ Digit. Med. 3, 1–7 (2020).
Google Scholar
Rodriguez-Chavez, I. R. & Licholai, G. in Digital Therapeutics (Chapman and Hall/CRC, 2022).
Stahlberg, E. et al. Exploring approaches for predictive cancer patient digital twins: opportunities for collaboration and innovation. Front. Digit. Health https://doi.org/10.3389/fdgth.2022.1007784 (2022).
Benson, M. Digital twins for predictive, preventive personalized, and participatory treatment of immune-mediated diseases. Arterioscler. Thromb. Vasc. Biol. 43, 410–416 (2023).
Google Scholar
Karakra, A. HospiT’Win: designing a discrete event simulation-based digital twin for real-time monitoring and near-future prediction of patient pathways in the hospital, Ecole des Mines d’Albi-Carmaux, (2021).
Bordukova, M., Makarov, N., Rodriguez-Esteban, R., Schmich, F. & Menden, M. P. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert Opin. Drug Discov. 19, 33–42 (2024).
Google Scholar
Lapid, M., Clarke, B. & Wright, S. Institutional review boards: what clinician researchers need to know. Mayo Clin. Proc. 94, 515 (2019).
Google Scholar
Weijer, C. The ethical analysis of risk. J. Law Med. Ethics 28, 344–361 (2000).
Google Scholar
Bruynseels, K., Santoni de Sio, F. & van den Hoven, J. Digital twins in health care: ethical implications of an emerging Engineering paradigm. Front. Genet. 9, 31 (2018).
Google Scholar
Wang, Y. et al. TWIN-GPT: digital twins for clinical trials via large language model. In ACM Transactions on Multimedia Computing, Communications and Applications (ACM, 2024).
Rahmim, A. et al. Theranostic digital twins for personalized radiopharmaceutical therapies: reimagining theranostics via computational nuclear oncology. Front. Oncol. 12, 1062592 (2022).
Google Scholar
Cellina, M. et al. Digital twins: the new frontier for personalized medicine?. Appl. Sci. 13, 7940 (2023).
Google Scholar
Mulder, S. T. et al. Dynamic digital twin: diagnosis, treatment, prediction, and prevention of disease during the life course. J. Med. Internet Res. 24, e35675 (2022).
Google Scholar
Akbarialiabad, H., Pasdar, A. & Murrell, D. F. Digital twins in dermatology, current status, and the road ahead. NPJ Digit. Med. 7, 228 (2024).
Google Scholar
An, G. & Cockrell, C. Drug development digital twins for drug discovery, testing and repurposing: a schema for requirements and development. Front. Syst. Biol. 2, 928387 (2022).
Google Scholar
Waight, M. C. et al. Personalized heart digital twins detect substrate abnormalities in scar-dependent ventricular tachycardia. Circulation 151, 521–533 (2025).
Google Scholar
Allen, A. et al. A digital twins machine learning model for forecasting disease progression in stroke patients. Appl. Sci. 11, 5576 (2021).
Google Scholar
Wong, C. H., Siah, K. W. & Lo, A. W. Estimation of clinical trial success rates and related parameters. Biostatistics 20, 273–286 (2019).
Google Scholar
Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M. & Granger, C. B. Fundamentals of Clinical Trials (Springer, 2015).
Brøgger-Mikkelsen, M., Ali, Z., Zibert, J. R., Andersen, A. D. & Thomsen, S. F. Online patient recruitment in clinical trials: systematic review and meta-analysis. J. Med. Internet Res. 22, e22179 (2020).
Google Scholar
Huang, P. -h, Kim, K. -h & Schermer, M. Ethical issues of digital twins for personalized health care service: preliminary mapping study. J. Med. Internet Res. 24, e33081 (2022).
Google Scholar
Thangaraj, P. M. et al. A novel digital twin strategy to examine the implications of randomized clinical trials for real-world populations. Preprint at medRxiv (2024).
Popa, E. O., van Hilten, M., Oosterkamp, E. & Bogaardt, M.-J. The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks. Life Sci. Soc. Policy 17, 1–25 (2021).
Google Scholar
Greenbaum, D. in Biocomputing 2021: Proceedings of the Pacific Symposium 38–49 (World Scientific, 2021).
Shapiro, M. A digital twin for individualized cardiology. HopkinsMedicine.org. https://www.hopkinsmedicine.org/news/articles/2025/05/a-digital-twin-for-individualized-cardiology (2025).
Abujarad, F. et al. Comparing a multimedia digital informed consent tool with traditional paper-based methods: randomized controlled trial. JMIR Form. Res. 5, e20458 (2021).
Google Scholar
Inan, O. T. et al. Digitizing clinical trials. NPJ Digit. Med. 3, 101 (2020).
Google Scholar
Jørgensen, C. S., Shukla, A. & Katt, B. in European Symposium on Research in Computer Security 140–153 (Springer).
Twin Health. Twin Health’s security overview. Twin Health security and compliance. https://usa.twinhealth.com/legal/security-and-compliance (2025).
Jameil, A. K. & Al-Raweshidy, H. A digital twin framework for real-time healthcare monitoring: leveraging AI and secure systems for enhanced patient outcomes. Discov. Internet Things 5, 37 (2025).
Google Scholar
Zhang, J. et al. Cyber resilience in healthcare digital twin on lung cancer. IEEE Access 8, 201900–201913 (2020).
Google Scholar
Rahman, H. U. et al. To explore the pharmacological mechanism of action using digital twin. Int. J. Adv. Appl. Sci. 9, 55–62 (2022).
Google Scholar
Subramanian, K. Digital twin for drug discovery and development—the virtual liver. J. Indian Inst. Sci. 100, 653–662 (2020).
Google Scholar
Faruqui, S. H. A. et al. Nurse-in-the-loop artificial intelligence for precision management of type 2 diabetes in a clinical trial utilizing transfer-learned predictive digital twin. preprint at arXiv:2401.02661 (2024).
Thorlund, K., Dron, L., Park, J. J. H. & Mills, E. J. Synthetic and external controls in clinical trials – a primer for researchers. Clin. Epidemiol. 12, 457–467 (2020).
Google Scholar
Wang, Z., Butner, J. D., Kerketta, R., Cristini, V. & Deisboeck, T. S. Simulating cancer growth with multiscale agent-based modeling. Semin. Cancer Biol. 30, 70–78 (2015).
Højbjerre-Frandsen, E., Jeppesen, M. L. & Jensen, R. K. Increasing the Power in Randomised Clinical Trials Using Digital Twins. Master’s thesis, Aalborg Univ. (2022).
Attaran, M., Attaran, S. & Celik, B. G. Revolutionizing agriculture through digital twins. Encyclopedia of Information Science and Technology, Sixth Edition. 1–14 (2025).
Lal, A., Dang, J., Nabzdyk, C., Gajic, O. & Herasevich, V. Regulatory oversight and ethical concerns surrounding software as medical device (SaMD) and digital twin technology in healthcare. Ann. Transl. Med. https://doi.org/10.21037/atm-22-4203 (2022).
Google Scholar
Zoltick, M. M. & Maisel, J. B. The Digital Twin (Springer, 2023).
Currie, G. M., Hawk, K. E. & Rohren, E. M. The potential role of artificial intelligence in sustainability of nuclear medicine.Radiography (Lond.) 30 (Suppl. 1), 119–124 https://doi.org/10.1016/j.radi.2024.03.005 (2024).
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM Comput. Surv. (CSUR) 54, 1–35 (2021).
Google Scholar
Mariam, Z., Niazi, S. K. & Magoola, M. Unlocking the future of drug development: generative AI, digital twins, and beyond. BioMedInformatics 4, 1441–1456 (2024).
Google Scholar
MacDonald, J. et al. Health technology for all: an equity-based paradigm shift opportunity. NAM Perspect. https://doi.org/10.31478/202212a (2022).
Ferlito, B., De Proost, M. & Segers, S. Navigating the landscape of digital twins in medicine: a relational bioethical inquiry. Asian Bioeth. Rev. 16, 471–481 (2024).
Google Scholar
Willison, R., Lowry, P. B. & Paternoster, R. A tale of two deterrents: considering the role of absolute and restrictive deterrence to inspire new directions in behavioral and organizational security research. J. Assoc. Inf. Syst. 19, 3 (2018).
Suhail, S., Jurdak, R. & Hussain, R. Security attacks and solutions for digital twins. Preprint at arXiv:2202.12501 (2022).
XM Cyber. How digital twins are revolutionizing threat management. https://xmcyber.com/blog/how-digital-twins-are-revolutionizing-threat-management/ (2025).
Nugent, T., Upton, D. & Cimpoesu, M. Improving data transparency in clinical trials using blockchain smart contracts. F1000Res 5, 2541 (2016).
Google Scholar
Amofa, S. et al. Blockchain-secure patient digital twin in healthcare using smart contracts. PLoS ONE 19, e0286120 (2024).
Google Scholar
Kasyapa, M. S. B. & Vanmathi, C. Blockchain integration in healthcare: a comprehensive investigation of use cases, performance issues, and mitigation strategies. Front. Digit. Health 6, 1359858 (2024).
Google Scholar
Hirano, T. et al. Data validation and verification using blockchain in a clinical trial for breast cancer: regulatory sandbox. J. Med. Internet Res. 22, e18938 (2020).
Google Scholar
