Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for 1.8 million deaths in 2020 (Sung et al., 2021). Despite major advances in imaging, molecular diagnostics, and targeted therapies, many patients are still diagnosed at advanced stages when curative options are limited. The heterogeneity of lung cancer, its aggressive nature, and the complex interplay of environmental and genetic factors make early detection and effective treatment highly challenging.
AI in Lung Cancer has emerged as a transformative technology across oncology. In lung cancer, AI applications span the entire disease continuum—from pathology and early detection to diagnosis, prognosis prediction, and personalized treatment planning. By analyzing vast, multimodal datasets including imaging, histopathology, genomics, and clinical records, AI systems can provide predictive insights, improve diagnostic accuracy, and optimize therapeutic strategies.
This article explores the role of AI in lung cancer across five major domains: pathology, detection, diagnosis, predictions, and treatment.
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AI in Lung Cancer Pathology
Histopathological assessment remains the gold standard for diagnosing lung cancer and determining tumor subtype. Traditionally, this relies on visual examination of tissue samples under a microscope. However, interobserver variability, the complexity of lung cancer subtypes, and the need for precise biomarker evaluation create challenges.
AI, particularly deep learning algorithms, has demonstrated remarkable performance in digital pathology. Convolutional neural networks (CNNs) can be trained on whole-slide images to automatically detect malignant regions, classify histological subtypes (e.g., adenocarcinoma, squamous cell carcinoma, small-cell lung cancer), and quantify tumor-infiltrating lymphocytes (Coudray et al., 2018).
Furthermore, AI-based pathology can identify molecular alterations directly from histology images. For example, studies have shown that AI can predict EGFR and KRAS mutation status from digital slides with significant accuracy (Kather et al., 2020). This reduces reliance on expensive molecular tests and accelerates clinical decision-making.
AI also supports quality assurance in pathology labs, ensuring consistency in sample interpretation and reducing diagnostic delays.
AI in Lung Cancer Detection
Early detection of lung cancer is critical to improving survival outcomes. Low-dose computed tomography (LDCT) screening has been shown to reduce mortality in high-risk populations, yet it is limited by high false-positive rates and interpretation challenges.
AI-enhanced imaging analysis addresses these issues by automatically detecting pulmonary nodules, characterizing their features, and distinguishing malignant from benign lesions. Google’s DeepMind and other research groups have developed deep learning systems that outperform radiologists in nodule detection and malignancy prediction (Ardila et al., 2019).
These models integrate temporal data, comparing nodules across serial scans to assess growth patterns. Such dynamic evaluation is difficult for human readers but highly suited for machine learning. AI also reduces radiologist workload by prioritizing suspicious scans, thereby enhancing efficiency in large-scale screening programs.
In addition, AI can integrate risk factors such as age, smoking history, and family history with imaging data to refine screening recommendations and reduce unnecessary interventions.
AI in Lung Cancer Diagnosis
Accurate diagnosis involves not only confirming malignancy but also classifying the tumor subtype and identifying actionable molecular alterations. AI supports this through integration of imaging, pathology, and molecular data.
Radiomics, the extraction of quantitative features from medical images, allows AI models to analyze tumor shape, texture, and intensity beyond what the human eye can perceive. Radiogenomics further links these imaging features with genomic alterations, enabling non-invasive prediction of driver mutations such as ALK or ROS1 rearrangements (Aerts et al., 2014).
In pathology, AI tools facilitate rapid immunohistochemistry interpretation, automate PD-L1 scoring for immunotherapy eligibility, and standardize diagnostic workflows.
Moreover, AI-driven diagnostic systems are being integrated into multidisciplinary tumor boards. By synthesizing clinical, pathological, and imaging data, these platforms can generate evidence-based diagnostic recommendations, supporting oncologists in complex cases.
AI in Lung Cancer Predictions
Prognostic and predictive modeling is another domain where AI shows promise. Traditional prognostic models, such as TNM staging, capture only part of the complexity of lung cancer outcomes. AI models, trained on multimodal datasets, can predict survival, recurrence risk, and treatment response more accurately.
For example, deep learning applied to CT imaging can stratify patients into risk categories for overall survival, outperforming conventional clinical models (Hosny et al., 2018). Similarly, machine learning models analyzing circulating tumor DNA (ctDNA) can identify minimal residual disease and predict relapse before radiographic progression (Abbosh et al., 2017).
In immunotherapy, AI can predict response based on imaging, PD-L1 expression, tumor mutational burden, and microenvironmental features. Predictive models are increasingly being used to select patients most likely to benefit from checkpoint inhibitors, avoiding unnecessary toxicity and costs.
These predictive capabilities also extend to drug resistance. AI models analyzing genomic evolution can forecast the emergence of resistance mutations, guiding early therapeutic adjustments.
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AI in Lung Cancer Treatment
AI contributes to treatment personalization and optimization across systemic therapy, surgery, and radiotherapy.
Precision oncology and systemic therapy
By integrating genomic, transcriptomic, and proteomic data, AI platforms can identify patient-specific vulnerabilities and suggest targeted therapies. Companies such as Tempus and Foundation Medicine employ AI algorithms to interpret sequencing results and recommend personalized treatment regimens.
AI also accelerates drug discovery in lung cancer. Machine learning models can design novel molecules targeting EGFR, KRAS G12C, or other oncogenic drivers, shortening the preclinical pipeline and enabling rapid testing (Zhavoronkov et al., 2019).
Surgery and perioperative planning
AI can assist thoracic surgeons by analyzing preoperative imaging to assess resectability, predict surgical outcomes, and reduce complications. Machine learning-based risk calculators provide individualized assessments of postoperative morbidity and mortality.
Radiotherapy optimization
Radiation oncology has been one of the most AI-driven fields. Automated contouring of tumors and organs at risk using deep learning saves time and standardizes treatment planning (Feng et al., 2022). Furthermore, AI models can personalize radiotherapy dose distributions based on tumor radiosensitivity and predict toxicities, improving therapeutic ratio.
Combination therapies and adaptive treatment
AI can simulate complex treatment scenarios, predicting synergistic effects of drug combinations. Adaptive treatment strategies, guided by AI analysis of interim imaging and biomarker data, allow clinicians to modify therapy in real time.
Ethical, Regulatory, and Practical Considerations
While AI holds immense promise, challenges remain. Data quality, bias, and interoperability are persistent issues. Most AI models are trained on retrospective, single-institution datasets, limiting generalizability. Regulatory frameworks are evolving, but questions about liability, transparency, and patient consent must be addressed.
Interpretability is another concern—clinicians may be hesitant to trust “black box” models without clear explanations. Efforts to develop explainable AI are crucial for clinical adoption.
Practical integration into workflows also requires infrastructure investment, interdisciplinary training, and clinician acceptance. Successful implementation depends on balancing technological advances with ethical and clinical realities.
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The Future of AI in Lung Cancer
The future will likely see AI embedded in every stage of lung cancer care. Multi-modal platforms integrating imaging, pathology, genomics, and clinical data into unified predictive models will drive precision oncology. Digital twins—virtual models of individual patients—may allow simulation of treatment outcomes before real-world application.
Collaborations between academia, industry, and regulatory agencies will be essential to ensure rigorous validation and equitable deployment. As federated learning techniques mature, data from multiple institutions can be used without compromising patient privacy, improving model robustness.
Ultimately, AI in lung cancer has the potential to transform outcomes by enabling earlier detection, more accurate diagnosis, individualized predictions, and optimized treatments.
Conclusion
Artificial intelligence is revolutionizing lung cancer care. From pathology to detection, diagnosis, prediction, and treatment, AI provides tools that enhance accuracy, efficiency, and personalization. While challenges in validation, ethics, and integration remain, the trajectory is clear: AI will play an increasingly central role in reducing the global burden of lung cancer.
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Written by Armen Gevorgyan, MD