Lung cancer (LC) remains the leading cause of cancer-related deaths worldwide, mainly due to late-stage diagnoses and the complexity of novel treatment pathways. However, artificial intelligence (AI) rapidly transforms LC management, improving early detection, optimizing treatment decisions, and personalizing care. As AI continues to integrate into clinical practice, the future of LC management is shifting toward precision-driven, data-informed strategies and risk prediction models.
Early detection: A game-changer
The success of LC treatment hinges on early detection. Low-dose CT screening has been a breakthrough in identifying lung nodules, yet false positives and interpretation variability remain challenging. AI-powered imaging analysis offers high-accuracy nodule detection and risk assessment, minimizing unnecessary biopsies.
Machine learning models trained on vast datasets can now distinguish between benign and malignant nodules with accuracy comparable with experienced radiologists. Beyond simple detection, AI can interpret growth patterns and morphological changes over time, offering a more dynamic risk stratification approach.1
Optimizing navigational bronchoscopy
More effective training methods have been developed using bronchial segment identification systems based on AI.2 AI systems have improved operational tools used during lymph node sampling, significantly improving the diagnostic yield of peripheral bronchoscopy by assisting different aspects of the bronchoscopic procedure.
The future of treatment decision-making
Personalized treatment strategies are at the heart of modern LC care. AI is pivotal in integrating multimodal data (imaging, genomics, pathology, and clinical records) to guide decision-making.2
AI-driven algorithms are assisting oncologists to predict the likelihood of response to therapies, including immunotherapy and targeted treatments. Additionally, AI-powered predictive models refine chemotherapy and immunotherapy selection by assessing patient-specific biomarkers and tumor microenvironment characteristics.3
Enhancing radiotherapy and surgical planning
Radiotherapy remains a cornerstone in LC management, and AI is helping optimize dose planning and target delineation. In surgical cases, AI is being integrated into robotic-assisted procedures, enhancing precision in tumor resections. Augmented reality overlays based on AI-analyzed imaging are helping surgeons visualize tumor margins in real time, reducing the risk of incomplete resections and improving long-term outcomes.4
Follow-up and survivorship
LC survivors require ongoing surveillance to detect recurrences and manage treatment-related complications. AI-powered surveillance models can analyze follow-up imaging and clinical data, identifying recurrence patterns earlier than conventional methods. Remote patient monitoring through AI-driven wearable devices can track physiological parameters, alerting clinicians to potential complications before they become critical.5
Challenges and the road ahead
Despite its promise, AI in LC management is not without challenges. Algorithmic bias, data privacy concerns, and robust clinical validation remain key hurdles. Additionally, AI should complement, not replace, clinical judgment. Training clinicians to integrate AI insights into decision-making without overreliance is essential.6
The Lung Cancer Section at CHEST continues to drive innovation in multidisciplinary LC care, fostering collaboration among pulmonologists, thoracic surgeons, medical and radiation oncologists, and radiologists. Initiatives will focus on integrating AI-driven approaches into LC management while discussing challenges in real-world implementation. The future of LC treatment lies in the synergy between human expertise and AI-driven insights, which will improve survival and quality of life for patients worldwide.
References
1. Bardoni C, Spaggiari L, Bertolaccini L. Artificial intelligence in lung cancer. Ann Transl Med. 2024;12(4):79.
2. Wang S, Yang DM, Rong R, et al. Artificial intelligence in lung cancer pathology image analysis. Cancers (Basel). 2019;11(11):1673.
3. Adegbesan A, Akingbola A, Aremu S, Adewole O, Amamdikwa JC, Shagaya U. From scalpels to algorithms: the risk of dependence on artificial intelligence in surgery. Journal of Medicine, Surgery, and Public Health. 2024;3.
4. Varghese C, Harrison EM, O’Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med. 2024;30(5):1257-1268.
5. Chiu HY, Chao HS, Chen YM. Application of artificial intelligence in lung cancer. Cancers (Basel). 2022;14(6):1370.
6. Furfaro D, Celi LA, Schwartzstein RM. Artificial intelligence in medical education: l Long way to go. Chest. 2024;165(4):771-774.