Generative AI and Large Language Models in Rehabilitation: A Scoping Review

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 2025-2026 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.