Editorial: Generative AI and large language models in medicine: applications, challenges, and opportunities
Abstract
Generative artificial intelligence has entered medicine through a heterogeneous set of uses rather than through a single technological pathway. Large language models now draft clinical text, interpret reports, answer patient questions, support knowledge retrieval, structure health data, assist education, and participate in increasingly complex decision support workflows. The 34 contributions assembled in this Research Topic capture that breadth across clinical medicine, public health, digital health, oncology, rehabilitation, mental health, nursing, health informatics, education, quality management, and biomedical policy. Their collective value lies less in any single performance estimate than in the opportunity to examine what changes when language model research moves from demonstrations of linguistic competence toward systems that must operate under clinical constraints.The studies concerned with diagnosis, staging, and clinical reasoning show why this distinction matters. In a two institution study of 552 PET CT reports across six cancers, Xu et al. found that GPT 5 achieved 82.1 percent overall TNM staging accuracy compared with 77.0 percent for junior radiologists, while repeated model agreement was close to perfect. Performance nevertheless differed across cancer types and was weakest for metastatic staging. Dinç et al. reported an analogous pattern in radiation oncology. GPT 5 reached 92.8 percent on a standardized examination benchmark, substantially exceeding…
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