{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:29:51Z","timestamp":1791646191530,"version":"4.3.4"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Generative artificial intelligence (GenAI), particularly large language models (LLMs) such as ChatGPT, GPT-3.5, and GPT-4, is rapidly being integrated into sports medicine practice. These tools are increasingly used by health professionals, coaches, and athletes for training prescription, rehabilitation, nutrition, mental health support, injury prevention, and academic writing. However, their adoption has outpaced the development of robust evidence regarding their clinical utility, accuracy, and safety, and no comprehensive synthesis of this emerging field currently exists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This scoping review aimed to map the current evidence on GenAI and LLM applications in sports medicine and athlete health, evaluate their accuracy and hallucination risks across domains, and synthesise reported ethical and governance concerns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The review followed PRISMA-ScR guidelines and Joanna Briggs Institute methodology. Six databases (PubMed\/MEDLINE, Scopus, Web of Science, SPORTDiscus, IEEE Xplore, and CINAHL) were searched for studies published between January 2022 and March 2026. Eligibility criteria were defined using the Population\u2013Concept\u2013Context framework. Two independent reviewers conducted screening, full-text assessment, and data extraction, with strong inter-rater agreement (\n                      <jats:italic>\u03ba<\/jats:italic>\n                      \u202f=\u202f0.82).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 1,847 records identified, 32 studies were included. Applications were classified into seven domains: training and exercise prescription (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f5), nutrition (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f4), rehabilitation (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f3), mental health (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f3), clinical decision support (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f5), academic writing (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f6), and ethics\/governance (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f6). LLM accuracy varied substantially: in a single validation study, content validity ratios for sleep recommendations ranged from 0.33 (GPT-3.5) to 0.67 (GPT-4), while only GPT-4 achieved acceptable validity for jet lag guidance (CVR\u202f=\u202f0.68). In one bibliometric analysis, AI-generated text in sports medicine journals increased from 2.38% (early 2023) to 6.25% (late 2024). Hallucination risk was rated critical for general-purpose chatbots but substantially reduced in retrieval-augmented systems.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>GenAI shows promise as a supervised decision-support tool in sports medicine, but current evidence does not support unsupervised clinical use. Key challenges include hallucination risks, a lack of sport-specific validation datasets, and insufficient ethical and governance frameworks. Addressing these gaps is essential before widespread integration into athlete health and public health contexts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1843535","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:58:12Z","timestamp":1785304692000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Generative artificial intelligence and large language models in sports medicine: a scoping review of applications, accuracy, and ethical implications"],"prefix":"10.3389","volume":"14","author":[{"given":"Ismail","family":"Dergaa","sequence":"first","affiliation":[{"name":"Higher Institute of Sport and Physical Education of Ksar Said, University of Manouba","place":["Manouba, Tunisia"]},{"name":"Physical Activity Research Unit, Sport and Health (UR18JS01), National Observatory of Sports","place":["Tunis, Tunisia"]},{"name":"Higher Institute of Sport and Physical Education of Kef, University of Jendouba","place":["Kef, Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed Amine","family":"Dergaa","sequence":"additional","affiliation":[{"name":"Higher Institute of Sport and Physical Education of Kef, University of Jendouba","place":["Kef, Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mortadha","family":"Razzak","sequence":"additional","affiliation":[{"name":"Higher Institute of Sport and Physical Education of Kef, University of Jendouba","place":["Kef, Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Halil \u0130brahim","family":"Ceylan","sequence":"additional","affiliation":[{"name":"Physical Education and Sports Teaching Department, Faculty of Sports Sciences, Atat\u00fcrk University","place":["Erzurum, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Stefanica","sequence":"additional","affiliation":[{"name":"Department of Physical Education and Sport, Faculty of Sciences, Physical Education and Informatics, National University of Science and Technology Politehnica Bucharest, Pitesti University Center","place":["Pitesti, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raul Ioan","family":"Muntean","sequence":"additional","affiliation":[{"name":"Department of Physical Education and Sport, Faculty of Law and Social Sciences, University \u201c1 Decembrie 1918\u201d of Alba Iulia","place":["Alba Iulia, Romania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noomen","family":"Guelmami","sequence":"additional","affiliation":[{"name":"Higher Institute of Sport and Physical Education of Kef, University of Jendouba","place":["Kef, Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s40634-021-00346-x","article-title":"Machine learning methods in sport injury prediction and prevention: a systematic review","volume":"8","author":"Van Eetvelde","year":"2021","journal-title":"J Exp Orthop"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"2469","DOI":"10.1007\/s40279-022-01698-9","article-title":"Just how confident can we be in predicting sports injuries? A systematic review of the methodological conduct and performance of existing musculoskeletal injury prediction models in sport","volume":"52","author":"Bullock","year":"2022","journal-title":"Sports Med"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"20552076241256745","DOI":"10.1177\/20552076241256745","article-title":"Advanced biomechanical analytics: wearable technologies for precision health monitoring in sports performance","volume":"10","author":"Alzahrani","year":"2024","journal-title":"Digit Health"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/bios16020097","article-title":"Wearable biosensing and machine learning for data-driven training and coaching support","volume":"16","author":"Madrigal-Cerezo","year":"2026","journal-title":"Biosensors"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s40798-019-0202-3","article-title":"Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review","volume":"5","author":"Claudino","year":"2019","journal-title":"Sports Med Open"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"233","DOI":"10.5114\/biolsport.2023.114283","article-title":"A review of machine learning applications in soccer with an emphasis on injury risk","volume":"40","author":"Nassis","year":"2023","journal-title":"Biol Sport"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"887","DOI":"10.3390\/bioengineering12080887","article-title":"Artificial intelligence in sports biomechanics: a scoping review on wearable technology, motion analysis, and injury prevention","volume":"12","author":"Souaifi","year":"2025","journal-title":"Bioengineering"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/02640414.2025.2518694","article-title":"Artificial intelligence in sport: a narrative review of applications, challenges and future trends","volume":"15","author":"Zhou","year":"2025","journal-title":"J Sports Sci"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1332427","DOI":"10.3389\/fspor.2024.1332427","article-title":"Challenges and opportunities of artificial intelligence implementation within sports science and sports medicine teams","volume":"6","author":"Naughton","year":"2024","journal-title":"Front Sports Act Living"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat Med"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"101047","DOI":"10.1016\/j.jshs.2025.101047","article-title":"Ethical implications of artificial intelligence in sport: a systematic scoping review","volume":"14","author":"Kim","year":"2025","journal-title":"J Sport Health Sci"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/d41586-023-00288-7","article-title":"ChatGPT: five priorities for research","volume":"614","author":"van Dis","year":"2023","journal-title":"Nature"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"887","DOI":"10.3390\/healthcare11060887","article-title":"ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns","volume":"11","author":"Sallam","year":"2023","journal-title":"Health"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"209","DOI":"10.5114\/biolsport.2024.132987","article-title":"Artificial intelligence in sport: exploring the potential of using ChatGPT in resistance training prescription","volume":"41","author":"Washif","year":"2024","journal-title":"Biol Sport"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"221","DOI":"10.5114\/biolsport.2024.133661","article-title":"Using artificial intelligence for exercise prescription in personalised health promotion: a critical evaluation of OpenAI's GPT-4 model","volume":"41","author":"Dergaa","year":"2024","journal-title":"Biol Sport"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1123\/ijspp.2023-0109","article-title":"ChatGPT for sample-size calculation in sports medicine and exercise sciences: a cautionary note","volume":"18","author":"Methnani","year":"2023","journal-title":"Int J Sports Physiol Perform"},{"key":"ref18","first-page":"257","article-title":"Can we trust them? An expert evaluation of large language models to provide sleep and jet lag recommendations for athletes","volume-title":"Sports Med","author":"Vitale","year":"2026"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"e51157","DOI":"10.2196\/51157","article-title":"Assessing ChatGPT's competency in addressing interdisciplinary inquiries on chatbot uses in sports rehabilitation","volume":"10","author":"McBee","year":"2024","journal-title":"JMIR Med Educ"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"23259671251371234","DOI":"10.1177\/23259671251371234","article-title":"Large language model-based writing in published sports medicine research: uncovering a growing influence","volume":"13","author":"Nassar","year":"2025","journal-title":"Orthop J Sports Med"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.11124\/JBIES-20-00167","article-title":"Updated methodological guidance for the conduct of scoping reviews","volume":"18","author":"Peters","year":"2020","journal-title":"JBI Evid Synth"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1277756","DOI":"10.3389\/fpsyt.2023.1277756","article-title":"ChatGPT is not ready yet for use in providing mental health assessment and interventions","volume":"14","author":"Dergaa","year":"2024","journal-title":"Front Psychol"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"e59309","DOI":"10.2196\/59309","article-title":"Using large language models to enhance exercise recommendations and physical activity in clinical and healthy populations: scoping review","volume":"13","author":"Lai","year":"2025","journal-title":"JMIR Med Inform"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"660","DOI":"10.3390\/nu18040660","article-title":"A maturation-aware machine learning framework for screening the nutritional status of adolescents","volume":"18","author":"Ghouili","year":"2026","journal-title":"Nutrients"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"e0325982","DOI":"10.1371\/journal.pone.0325982","article-title":"The sports nutrition knowledge of large language model (LLM) artificial intelligence (AI) chatbots: An assessment of accuracy, completeness, clarity, quality of evidence, and test-retest reliability","volume":"20","author":"Solomon","year":"2025","journal-title":"PLoS One"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"305","DOI":"10.5114\/biolsport.2024.141063","article-title":"Optimizing athletic performance through advanced nutrition strategies: can AI and digital platforms have a role in ultraendurance sports?","volume":"41","author":"Puce","year":"2024","journal-title":"Biol Sport"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"3209","DOI":"10.3390\/nu17203209","article-title":"Artificial intelligence in endurance sports: metabolic, recovery, and nutritional perspectives","volume":"17","author":"Grivas","year":"2025","journal-title":"Nutrients"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"222","DOI":"10.2519\/jospt.2024.12151","article-title":"Performance of ChatGPT compared to clinical practice guidelines in making informed decisions for lumbosacral radicular pain: a cross-sectional study","volume":"54","author":"Gianola","year":"2024","journal-title":"J Orthop Sports Phys Ther"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1055\/s-0043-1778019","article-title":"The future of artificial intelligence in sports medicine and return to play","volume":"28","author":"Desai","year":"2024","journal-title":"Semin Musculoskelet Radiol"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1136\/bjsports-2019-100715","article-title":"Mental health in elite athletes: International Olympic Committee consensus statement","volume":"53","author":"Reardon","year":"2019","journal-title":"Br J Sports Med"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"e14627","DOI":"10.1111\/sms.14627","article-title":"First international consensus statement on sports psychiatry","volume":"34","author":"Claussen","year":"2024","journal-title":"Scand J Med Sci Sports"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"e22755","DOI":"10.2196\/22755","article-title":"Psychological screening and tracking of athletes and digital mental health solutions in a hybrid model of care: mini review","volume":"4","author":"Balcombe","year":"2020","journal-title":"JMIR Form Res"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"348","DOI":"10.3390\/s24020348","article-title":"Machine learning for multimodal mental health detection: a systematic review of passive sensing approaches","volume":"24","author":"Khoo","year":"2024","journal-title":"Sensors"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"1502417","DOI":"10.3389\/fnins.2025.1502417","article-title":"Physical activity and neuroplasticity in neurodegenerative disorders: a comprehensive review","volume":"19","author":"Ben Ezzdine","year":"2025","journal-title":"Front Neurosci"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"1259845","DOI":"10.3389\/fpsyg.2024.1259845","article-title":"From tools to threats: a reflection on the impact of artificial-intelligence chatbots on cognitive health","volume":"15","author":"Dergaa","year":"2024","journal-title":"Front Psychol"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"615","DOI":"10.5114\/biolsport.2023.125623","article-title":"From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing","volume":"40","author":"Dergaa","year":"2023","journal-title":"Biol Sport"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"8934","DOI":"10.3390\/jcm14248934","article-title":"Early prediction of acute respiratory distress syndrome in critically ill polytrauma patients using balanced random forest ML: a retrospective cohort study","volume":"14","author":"Hassine","year":"2025","journal-title":"J Clin Med"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3390\/bioengineering13010030","article-title":"Accurate clinical entity recognition and code mapping of anatomopathological reports using BioClinicalBERT enhanced by retrieval-augmented generation: a hybrid deep learning approach","volume":"13","author":"Abdaoui","year":"2025","journal-title":"Bioengineering (Basel)"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"e001568","DOI":"10.1136\/bmjsem-2023-001568","article-title":"AI did not write this manuscript, or did it? Can we trick the AI text detector into generated texts?","volume":"9","author":"Anderson","year":"2023","journal-title":"BMJ Open Sport Exerc Med"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"e54345","DOI":"10.2196\/54345","article-title":"Reference hallucination score for medical artificial intelligence chatbots: development and usability study","volume":"12","author":"Aljamaan","year":"2024","journal-title":"JMIR Med Inform"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"e39238","DOI":"10.7759\/cureus.39238","article-title":"High rates of fabricated and inaccurate references in ChatGPT-generated medical content","volume":"15","author":"Bhattacharyya","year":"2023","journal-title":"Cureus"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"109404","DOI":"10.1016\/j.identj.2025.109404","article-title":"MedFusionT5: cross-modal attention boosts semantic quality and reduces hallucinations in dental AI","volume":"76","author":"Abdaoui","year":"2026","journal-title":"Int Dent J"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/s13102-026-01559-2","article-title":"How accurately do large language models interpret sport safeguarding principles: an evaluation using the International Olympic Committee framework","volume":"18","author":"G\u00f6kmen","year":"2026","journal-title":"BMC Sports Sci Med Rehabil"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"e6265","DOI":"10.62438\/tunismed.v103i11.6265","article-title":"Can generative AI improve academic journal selection decisions? Assessing traditional and algorithmic approaches in medical research","volume":"103","author":"Methnani","year":"2025","journal-title":"Tunis Med"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"2594","DOI":"10.3390\/healthcare13202594","article-title":"Advancing compliance with HIPAA and GDPR in healthcare: a blockchain-based strategy for secure data exchange in clinical research","volume":"13","author":"Barbaria","year":"2025","journal-title":"Healthcare (Basel)"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"1538807","DOI":"10.3389\/frai.2025.1538807","article-title":"Machine learning applications in the analysis of sedentary behavior and associated health risks","volume":"8","author":"Hammad","year":"2025","journal-title":"Front Artif Intell"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"101054","DOI":"10.1016\/j.jshs.2025.101054","article-title":"Artificial intelligence in health and sport sciences: promise, progress, and prudence","volume":"14","author":"An","year":"2025","journal-title":"J Sport Health Sci"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"e50048","DOI":"10.2196\/50048","article-title":"Toward fairness, accountability, transparency, and ethics in AI for social media and health care: scoping review","volume":"12","author":"Singhal","year":"2024","journal-title":"JMIR Med Inform"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1136\/bjsports-2020-102955","article-title":"World Health Organization 2020 guidelines on physical activity and sedentary behaviour","volume":"54","author":"Bull","year":"2020","journal-title":"Br J Sports Med"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/sms.12581","article-title":"Exercise as medicine: evidence for prescribing exercise as therapy in 26 different chronic diseases","volume":"25","author":"Pedersen","year":"2015","journal-title":"Scand J Med Sci Sports"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"e39757","DOI":"10.1097\/MD.0000000000039757","article-title":"A thorough examination of ChatGPT-3.5 potential applications in medical writing: a preliminary study","volume":"103","author":"Dergaa","year":"2024","journal-title":"Medicine (Baltimore)"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1843535\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:58:12Z","timestamp":1785304692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1843535\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,29]]},"references-count":52,"alternative-id":["10.3389\/fpubh.2026.1843535"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1843535","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,29]]},"article-number":"1843535"}}