{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T11:08:56Z","timestamp":1789124936839,"version":"build-2803163510"},"reference-count":136,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>Generative artificial intelligence (GAI), particularly large language models (LLMs) and multimodal foundation models, represents a new generation of artificial intelligence technologies with emerging applications in healthcare. In lung cancer care, where clinical decisions increasingly require integration of imaging, pathology, molecular alterations, and rapidly evolving therapeutic evidence, GAI provides new opportunities to enhance clinical information synthesis and decision support. Recent studies have explored the application of GAI across the lung cancer care continuum, including screening and early detection, diagnosis and characterization, treatment decision-making, prognosis and disease monitoring, patient\u2013clinician communication, and clinical workflow optimization. For example, multimodal foundation models such as M3FM have demonstrated the feasibility of jointly learning imaging and clinical information to support multiple lung cancer-related tasks within a unified architecture. In addition, oncology-specific LLMs trained on real-world clinical data have shown promise in predicting lung cancer progression by integrating longitudinal radiological and clinical information. However, most current applications remain at an exploratory or early validation stage, with substantial heterogeneity in model architectures, evaluation frameworks, and levels of clinical validation. Challenges related to evidence quality, data integration, safety, transparency, and regulatory oversight must be addressed before widespread clinical implementation. In this review, we provide a clinically oriented overview of the current applications of GAI across the lung cancer care continuum and discuss emerging developments, limitations, and future directions, including domain-specific models, multimodal systems, guideline-integrated decision support, and prospective validation frameworks.<\/jats:p>","DOI":"10.3389\/fonc.2026.1915669","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:40:49Z","timestamp":1789123249000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions"],"prefix":"10.3389","volume":"16","author":[{"given":"Wenzheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Beijing University of Chinese Medicine","place":["Beijing, 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