Generative and Large-Scale Artificial Intelligence in Exercise and Sports Medicine: A Narrative Review

Generative artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, has rapidly advanced in capability and accessibility, creating novel paradigms for personalized healthcare. In exercise and sports medicine, where clinical decision-making necessitates the integration of complex physiological data, individualized programming, and patient-centered communication, generative AI offers transformative potential for workflow augmentation. This narrative review synthesizes current applications, strengths, and limitations across seven core domains: (1) personalized exercise prescription, (2) performance enhancement and training support, (3) clinical rehabilitation and disease management, (4) lifestyle modification, (5) education and communication, (6) injury prevention, and (7) data analytics. LLMs demonstrated the ability to generate structured exercise prescriptions and rehabilitation protocols with moderate to high guideline compliance across cardiac and musculoskeletal rehabilitation contexts, while patient education content achieved favorable readability and clinical relevance ratings. Furthermore, methodological advancements such as prompt engineering and wearable-integrated closed-loop systems have enhanced personalization and real-time adaptability. In the domain of patient communication, generative AI tools produced readable educational materials with high factual consistency, although challenges persist regarding comorbidity screening, individualized safety verification, and cultural-linguistic contextualization. Ultimately, generative AI is poised to function as a first-draft accelerator and productivity amplifier within exercise and sports medicine. However, mandatory expert oversight, rigorous clinical validation, and robust governance frameworks remain essential prerequisites for the safe and effective integration of this approach into frontline clinical practice.