{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:29:53Z","timestamp":1791646193164,"version":"4.3.4"},"reference-count":74,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Generative artificial intelligence (AI) and large language models (LLMs) are increasingly evaluated in rehabilitation, yet their clinical validity, reproducibility, and safety remain uncertain. This scoping review mapped peer-reviewed studies of generative AI\/LLMs across rehabilitation assessment, clinical reasoning, decision support, planning, education, and functional classification. Following JBI methodology and PRISMA-ScR, five databases were searched for English-language studies published from 1 January 2015 to 24 July 2026. Two reviewers independently conducted study selection, data extraction, methodological appraisal, and application-domain coding. Of 2126 records, 43 publications representing 42 unique studies were included, predominantly from 2025\u20132026 and involving GPT\/ChatGPT\/OpenAI-family systems. At the unique-study level, six application domains were identified: clinical reasoning and decision support (n = 13), rehabilitation education, simulation, and feedback (n = 10), rehabilitation planning and prescription (n = 9), guideline adherence and clinical-question support (n = 6), adaptive feedback and rehabilitation support (n = 2), and assessment and functional classification (n = 2). Evidence was concentrated in benchmark, scenario-based, and educational evaluations, with limited patient-level outcomes. Heterogeneous methods, incomplete reporting of reproducibility, inconsistent safety assessment, and possible selective publication limited comparability and clinical generalizability. Generative AI\/LLMs should therefore be used primarily as clinician-supervised assistive tools, with prospective validation, standardized reporting, and active safety evaluation prioritized.<\/jats:p>","DOI":"10.3390\/life16081310","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T09:24:53Z","timestamp":1786353893000},"page":"1310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generative AI and Large Language Models in Rehabilitation: A Scoping Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3441-7495","authenticated-orcid":false,"given":"Su-Min","family":"Cha","sequence":"first","affiliation":[{"name":"Department of Occupational Therapy, Kyungnam University, Changwon 51767, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large Language Models in Medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large Language Models Encode Clinical Knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1146\/annurev-biodatasci-103123-095332","article-title":"Generative Artificial Intelligence in Medicine","volume":"8","author":"Shanmugam","year":"2025","journal-title":"Annu. Rev. Biomed. Data Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e59823","DOI":"10.2196\/59823","article-title":"The Clinicians\u2019 Guide to Large Language Models: A General Perspective with a Focus on Hallucinations","volume":"14","author":"Roustan","year":"2025","journal-title":"Interact. J. Med. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Basu, S., and Huynh, B. (2026). Mitigating Hallucinations in Healthcare AI: A Systematic Review of Evidence-Based Strategies. BMC Health Serv. Res., in press.","DOI":"10.1186\/s12913-026-14851-1"},{"key":"ref_6","first-page":"e39238","article-title":"High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical Content","volume":"15","author":"Bhattacharyya","year":"2023","journal-title":"Cureus"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Luo, M.S., Duan, Z., Gao, J., Sun, Y., and Chen, L. (2025). Evaluating the Role of ChatGPT in Rehabilitation Medicine: A Narrative Review. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1618510"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bai, T., Jiang, K., Yu, Y., Qie, S., and Wang, C. (2026). A Review of Research on the Applications of Large Models in Each Functional Module of the Entire Rehabilitation Process. Future Internet, 18.","DOI":"10.3390\/fi18020095"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"m3164","DOI":"10.1136\/bmj.m3164","article-title":"Reporting Guidelines for Clinical Trial Reports for Interventions Involving Artificial Intelligence: The CONSORT-AI Extension","volume":"370","author":"Liu","year":"2020","journal-title":"BMJ"},{"key":"ref_10","first-page":"m3210","article-title":"Guidelines for Clinical Trial Protocols for Interventions Involving Artificial Intelligence: The SPIRIT-AI Extension","volume":"370","author":"Liu","year":"2020","journal-title":"BMJ"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting Guideline for the Early-Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence: DECIDE-AI","volume":"28","author":"Vasey","year":"2022","journal-title":"Nat. Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Aromataris, E., and Munn, Z. (2020). Chapter 11: Scoping Reviews. JBI Manual for Evidence Synthesis, JBI.","DOI":"10.46658\/JBIRM-20-02"},{"key":"ref_13","doi-asserted-by":"crossref","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":"ref_14","first-page":"285","article-title":"The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers","volume":"34","author":"Hong","year":"2018","journal-title":"Educ. Inf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"494","DOI":"10.11124\/JBIES-22-00430","article-title":"The revised JBI critical appraisal tool for the assessment of risk of bias for randomized controlled trials","volume":"21","author":"Barker","year":"2023","journal-title":"JBI Evid. Synth."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"378","DOI":"10.11124\/JBIES-23-00268","article-title":"The revised JBI critical appraisal tool for the assessment of risk of bias for quasi-experimental studies","volume":"22","author":"Barker","year":"2024","journal-title":"JBI Evid. Synth."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Aromataris, E., and Munn, Z. (2020). Chapter 7: Systematic reviews of etiology and risk. JBI Manual for Evidence Synthesis, JBI.","DOI":"10.46658\/JBIRM-17-01"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"548","DOI":"10.7326\/ANNALS-25-02104","article-title":"QUADAS-3: A revised tool for the quality assessment of diagnostic test accuracy studies","volume":"179","author":"Whiting","year":"2026","journal-title":"Ann. Intern. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bandara, E., Gore, R., Shetty, S., Mukkamala, R., Rhea, C.K., Samulski, B.S., Hass, A., Yarlagadda, A., Kaushik, S., and De Silva, M. (2026). Standardization of Neuromuscular Reflex Analysis\u2014Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM-Enabled Decision Support System. Biomechanics, 6.","DOI":"10.3390\/biomechanics6010023"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bezruk, V., Ivanov, D., Ivanchuk, M., Shkrobanets, I., Makarova, O., Rynzhuk, L., Bulyk, T., Maksymiv, O., and Ivanova, M. (2026). Impact of Query Language on the Structure and Guideline Alignment of AI-Generated Rehabilitation Programs in Chronic Kidney Disease. J. Clin. Med., 15.","DOI":"10.3390\/jcm15114218"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s12984-026-01903-0","article-title":"Benchmarking Large Language Models against Human Experts in Rehabilitation Medicine: A Multidimensional Evaluation","volume":"23","author":"Cao","year":"2026","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2695784","DOI":"10.1080\/01443615.2026.2695784","article-title":"Evaluating AI in Postoperative Rehabilitation of Axillary Web Syndrome after Breast Cancer Surgery: ChatGPT vs. DeepSeek","volume":"46","author":"Cheng","year":"2026","journal-title":"J. Obstet. Gynaecol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dah\u00f2, M., and Caci, B. (2026). Exploring AI-Assisted Design of Executive Function Rehabilitation Programs for Individuals with ADHD: A Mixed-Methods Evaluation of Prompts and ChatGPT Outputs. BMC Psychol., 14.","DOI":"10.1186\/s40359-025-03729-2"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1177\/03080226251368231","article-title":"Artificial Intelligence in Occupational Therapy: Comparing ChatGPT and Occupational Therapist Approaches in Assessment and Intervention","volume":"89","author":"Kars","year":"2026","journal-title":"Br. J. Occup. Ther."},{"key":"ref_25","first-page":"16","article-title":"Agreement of ChatGPT with Clinical Practice Guidelines for Knee Osteoarthritis Treatment","volume":"24","author":"Garcia","year":"2026","journal-title":"Internet J. Allied Health Sci. Pract."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hamrin Senorski, E., Piussi, R., Kaarre, J., Feldt, R., Webster, K.E., H\u00e4gglund, M., Hamrin Senorski, R., H\u00f6gberg, J., Yu, Y., and Samuelsson, K. (2026). GPT-4 Outperforms Junior Expert Physical Therapists in Sports Medicine Rehabilitation: An Evaluation of AI Response Quality and Adaptiveness. Front. Rehabil. Sci., 7.","DOI":"10.3389\/fresc.2026.1853016"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kaya, O., Huri, G., \u00d6zbek, E.A., G\u00f6nder, N., Demir, \u0130.H., and Dalk\u0131r, K.A. (2026). Large Language Models in Sports Injury Care: A Comparative Expert Evaluation of GPT-4o and GPT-5. BMC Sports Sci. Med. Rehabil., 18.","DOI":"10.1186\/s13102-026-01675-z"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Knauer, R., Kalmring, M., and Rodner, E. (2026). Intelligence without Intuition: A Mixed-Methods Pilot Study on Reasoning Models in Musculoskeletal Physiotherapy for Low-Back Pain. Front. Digit. Health, 8.","DOI":"10.3389\/fdgth.2026.1789412"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, R., Liu, Q., Hu, Q., Nan, R., He, J., Yang, J., Zhang, J., Yang, G., Yang, Z., and Xiao, X. (2026). Tests of Large Language Models\u2019 Medical Competence and Application for Clinical Decision Support of Musculoskeletal Rehabilitation. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1719340"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, J.H., Wang, Y.L., Zhao, M.J., Yin, J.L., Ding, D.F., and Wu, X.B. (2026). Deficiencies in Clinical Reasoning of LLMs in Low Back Pain Management and Remediation via Prompt Engineering: From Performance Evaluation to Error Diagnosis. Front. Artif. Intell., 9.","DOI":"10.3389\/frai.2026.1811701"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Michou, I., Fouras, A., Chrysanthakopoulou, D., Theodoritsi, M., Mariettou, S., Stellatou, S., and Koutsojannis, C. (2026). AI-Powered Physiotherapy: Evaluating LLMs against Students in Clinical Rehabilitation Scenarios. Appl. Sci., 16.","DOI":"10.3390\/app16031165"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sortino, M., Trovato, B., Chiaramonte, R., Carrera, A., Sapienza, M., Roggio, F., and Musumeci, G. (2026). The Role of Artificial Intelligence and Professional Expertise in Adapted Physical Activity Prescription for Orthopedic Rehabilitation. J. Funct. Morphol. Kinesiol., 11.","DOI":"10.3390\/jfmk11010113"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sozlu, U., Gunay, S.M., and Ozkazanc, G. (2026). AI Assisted Photographic Posture Evaluation: A Comparative Study of ChatGPT and Expert Physiotherapists. BMC Sports Sci. Med. Rehabil., advance online publication.","DOI":"10.1186\/s13102-026-01812-8"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1007\/s00586-026-09795-3","article-title":"Multi-Criteria Evaluation of Clinical Decision-Making Performance in Spinal Neurosurgery and Physical Therapy Scenarios: A Comparative Analysis of Artificial Intelligence Models","volume":"35","author":"Tuncer","year":"2026","journal-title":"Eur. Spine J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Villagr\u00e1n, I., Hilliger, I., Rammsy, F., Fuentes-Cimma, J., Miranda, C., Paz-Martin, S., Varas, J., and Neyem, A. (2026). Human\u2013AI Feedback in Clinical Interview Training: Evidence and Lessons in Physiotherapy Education. Assess. Eval. High. Educ., advance online publication.","DOI":"10.1080\/02602938.2026.2644511"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"pzaf010","DOI":"10.1093\/ptj\/pzaf010","article-title":"Evaluating the Potential of Large Language Models for Vestibular Rehabilitation Education: A Comparison of ChatGPT, Google Gemini, and Clinicians","volume":"105","author":"Arbel","year":"2025","journal-title":"Phys. Ther."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"230","DOI":"10.22141\/2307-1257.14.3.2025.530","article-title":"Analysis of the Use of Artificial Intelligence Systems for the Development of Physical Exercise Programs during Rehabilitation of Nephrology Patients","volume":"14","author":"Bezruk","year":"2025","journal-title":"Kidneys"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3267","DOI":"10.1007\/s00405-025-09295-y","article-title":"Is There Any Room for ChatGPT AI Bot in Speech-Language Pathology?","volume":"282","author":"Birol","year":"2025","journal-title":"Eur. Arch. Otorhinolaryngol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/10400435.2024.2416495","article-title":"Using ChatGPT as a Tool during Occupational Therapy Intervention: A Case Report in Mild Cognitive Impairment","volume":"37","year":"2025","journal-title":"Assist. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ergezen Sahin, G., Aras Bayram, G., Sanchez Sierra, A., Akdemir, S., Kurc, D., Tarakci, D., and Tunali, A.N. (2025). Effects of Artificial Intelligence Based Physiotherapy Educational Approach in Developing Clinical Reasoning Skills: A Randomized Controlled Trial. BMC Med. Educ., 25.","DOI":"10.1186\/s12909-025-07926-w"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e66126","DOI":"10.2196\/66126","article-title":"Feasibility of a Randomized Controlled Trial of Large AI-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students: Pilot Randomized Parallel-Group Study","volume":"9","year":"2025","journal-title":"JMIR Form. Res."},{"key":"ref_42","first-page":"173","article-title":"Artificial Intelligence in Cardiac Rehabilitation: Assessing ChatGPT\u2019s Knowledge and Clinical Scenario Responses","volume":"53","year":"2025","journal-title":"Turk Kardiyol. Dern. Ars."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s10916-025-02207-x","article-title":"The Role of Artificial Intelligence Large Language Models in Personalized Rehabilitation Programs for Knee Osteoarthritis: An Observational Study","volume":"49","author":"Tuncay","year":"2025","journal-title":"J. Med. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"487","DOI":"10.54005\/geneltip.1634118","article-title":"Artificial Intelligence Exercise Recommendations in Knee Osteoarthritis Rehabilitation: ChatGPT-4o and Gemini Advanced Example","volume":"35","author":"Tuncay","year":"2025","journal-title":"Genel T\u0131p Derg."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e70177","DOI":"10.1002\/msc.70177","article-title":"Artificial Intelligence in Physical Therapy Education: Evaluating Clinical Reasoning Performance in Musculoskeletal Care Using ChatGPT","volume":"23","author":"Hao","year":"2025","journal-title":"Musculoskelet. Care"},{"key":"ref_46","first-page":"e83761","article-title":"ChatGPT and Occupational Therapy: A Study of Generated Program Feasibility","volume":"17","author":"Inoue","year":"2025","journal-title":"Cureus"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lee, S.A., and Park, J.H. (2025). The Usability of Neurological Occupational Therapy Case Studies Generated by ChatGPT. Healthcare, 13.","DOI":"10.3390\/healthcare13111341"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Marcaccini, G., Seth, I., Novo, J., McClure, V., Sacks, B., Lim, K., Ng, S.K.H., Cuomo, R., and Rozen, W.M. (2025). Leveraging Artificial Intelligence for Personalized Rehabilitation Programs for Head and Neck Surgery Patients. Technologies, 13.","DOI":"10.3390\/technologies13040142"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"753","DOI":"10.36740\/WLek\/203850","article-title":"AI-Driven Rehabilitation: Evaluation of ChatGPT-4o for Generating Personalized Physical Rehabilitation Plans in Comorbid Patients","volume":"78","author":"Mykhalko","year":"2025","journal-title":"Wiad. Lek."},{"key":"ref_50","first-page":"e94390","article-title":"Implementation and Educational Impact of a Story-Centered Curriculum Using a Large Language Model: A Class on Internal Disorders for Physiotherapy Students","volume":"17","author":"Okuno","year":"2025","journal-title":"Cureus"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rossettini, G., Bargeri, S., Cook, C., Guida, S., Palese, A., Rodeghiero, L., Pillastrini, P., Turolla, A., Castellini, G., and Gianola, S. (2025). Accuracy of ChatGPT-3.5, ChatGPT-4o, Copilot, Gemini, Claude, and Perplexity in Advising on Lumbosacral Radicular Pain against Clinical Practice Guidelines: Cross-Sectional Study. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1574287"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Safran, E., and Yildirim, S. (2025). A Cross-Sectional Study on ChatGPT\u2019s Alignment with Clinical Practice Guidelines in Musculoskeletal Rehabilitation. BMC Musculoskelet. Disord., 26.","DOI":"10.1186\/s12891-025-08650-8"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Saglam, S., Uludag, V., Karaduman, Z.O., Ar\u0131can, M., Y\u00fccel, M.O., and Dalaslan, R.E. (2025). Comparative Evaluation of Artificial Intelligence Models GPT-4 and GPT-3.5 in Clinical Decision-Making in Sports Surgery and Physiotherapy: A Cross-Sectional Study. BMC Med. Inform. Decis. Mak., 25.","DOI":"10.1186\/s12911-025-02996-8"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e85614","DOI":"10.2196\/85614","article-title":"Evaluation of Few-Shot AI-Generated Feedback on Case Reports in Physical Therapy Education: Mixed Methods Study","volume":"11","author":"Sudo","year":"2025","journal-title":"JMIR Med. Educ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1186\/s13018-025-05831-y","article-title":"Assessment of ChatGPT\u2019s Adherence to Evidence-Based Clinical Practice Guidelines for Plantar Fasciitis Management","volume":"20","author":"Zhang","year":"2025","journal-title":"J. Orthop. Surg. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"14084","DOI":"10.1038\/s41598-025-99216-0","article-title":"Comparison of Triage Performance among DRP Tool, ChatGPT, and Outpatient Rehabilitation Doctors","volume":"15","author":"Zou","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.1080\/09593985.2023.2291656","article-title":"Clinical Reasoning Using ChatGPT: Is It beyond Credibility for Physiotherapists Use?","volume":"40","author":"Bilika","year":"2024","journal-title":"Physiother. Theory Pract."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mansour, T., and Wong, J. (2024). Enhancing Fieldwork Readiness in Occupational Therapy Students with Generative AI. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1485325"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1589\/jpts.36.234","article-title":"Evaluation of the Accuracy of ChatGPT\u2019s Responses to and References for Clinical Questions in Physical Therapy","volume":"36","author":"Sawamura","year":"2024","journal-title":"J. Phys. Ther. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"7626","DOI":"10.1038\/s41598-024-58514-9","article-title":"GPT-Agents Based on Medical Guidelines Can Improve the Responsiveness and Explainability of Outcomes for Traumatic Brain Injury Rehabilitation","volume":"14","author":"Li","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"jrm13373","DOI":"10.2340\/jrm.v55.13373","article-title":"Use of Artificial Intelligence Large Language Models as a Clinical Tool in Rehabilitation Medicine: A Comparative Test Case","volume":"55","author":"Zhang","year":"2023","journal-title":"J. Rehabil. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/s41746-025-01565-7","article-title":"A Scoping Review on Generative AI and Large Language Models in Mitigating Medication Related Harm","volume":"8","author":"Ong","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Wang, H., Liu, Y., Hu, L., and Wang, X. (2026). Applications of Natural Language Processing and Large Language Models in Sports Injury Assessment and Rehabilitation Decision-Making: A Scoping Review. Front. Med., 13.","DOI":"10.3389\/fmed.2026.1866874"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ogut, E. (2025). Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery. Clin. Pract., 15.","DOI":"10.3390\/clinpract15090169"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e88057","DOI":"10.2196\/88057","article-title":"Large Language Models and Their Applications in Mental Health: Scoping Review","volume":"13","author":"Lokadjaja","year":"2026","journal-title":"JMIR Ment. Health"},{"key":"ref_66","unstructured":"White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., and Schmidt, D.C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41591-024-03425-5","article-title":"The TRIPOD-LLM Reporting Guideline for Studies Using Large Language Models","volume":"31","author":"Gallifant","year":"2025","journal-title":"Nat. Med."},{"key":"ref_68","doi-asserted-by":"crossref","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":"ref_69","unstructured":"World Health Organization (2024). Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models, WHO."},{"key":"ref_70","unstructured":"International Medical Device Regulators Forum (2014). Software as a Medical Device: Possible Framework for Risk Categorization and Corresponding Considerations, IMDRF."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e081554","DOI":"10.1136\/bmj-2024-081554","article-title":"FUTURE-AI: International Consensus Guideline for Trustworthy and Deployable Artificial Intelligence in Healthcare","volume":"388","author":"Lekadir","year":"2025","journal-title":"BMJ"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1145\/3571730","article-title":"Survey of Hallucination in Natural Language Generation","volume":"55","author":"Ji","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_73","unstructured":"World Health Organization (2021). Ethics and Governance of Artificial Intelligence for Health: WHO Guidance, World Health Organization."},{"key":"ref_74","unstructured":"Samek, W., Wiegand, T., and M\u00fcller, K.-R. (2017). Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. arXiv."}],"container-title":["Life"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/8\/1310\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T09:44:55Z","timestamp":1786355095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/8\/1310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,10]]},"references-count":74,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["life16081310"],"URL":"https:\/\/doi.org\/10.3390\/life16081310","relation":{},"ISSN":["2075-1729"],"issn-type":[{"value":"2075-1729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,10]]}}}