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Generative AI and Large Language Models in Rehabilitation: A Scoping Review
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DOI：10.3390/life16081310.png)
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Abstract
En 中文
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.
Keywords:
generative artificial intelligence
large language models
rehabilitation
clinical reasoning
clinical decision support
rehabilitation planning
prompt engineering
Journal
L
IF:
3.4
Papers:
154
Citations:
1
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Cited Papers