Generative Artificial Intelligence in Rehabilitation: A Systematic Mapping Review

Abstract

Background: Generative artificial intelligence (GenAI) is rapidly entering rehabilitation, but the maturity and functional distribution of the evidence remain unclear. Methods: We conducted a mapping review reported in accordance with PRISMA-ScR as applicable. PubMed and Scopus were searched using controlled vocabulary and free-text terms for GenAI, large language models, and rehabilitation. After deduplication, the records were manually screened by title and abstract, and full texts were consulted for uncertainty. Results: Of 1313 unique records, 233 met the eligibility criteria. Publications appeared from 2023 to 2026. Multi-label coding identified rehabilitation clinical and workflow support in 106/233 records (45%), patient-level rehabilitation intervention or functional assistance in 64/233 (27%), rehabilitation professional education in 56/233 (24%), and patient or caregiver education within rehabilitation in 45/233 (19%). Twenty-eight records (12%) addressed more than one application. Model/output evaluation or technical development predominated (155/233; 67%), whereas 13/233 records (6%) were interventional or feasibility studies. Conclusions: GenAI research in rehabilitation has expanded rapidly, but its most frequent use is clinician-facing support rather than direct patient-level intervention. The evidence remains dominated by technical, simulated, and observational evaluations, with limited clinical experimentation. Future research should prioritize prospective patient-level evaluation, transparent model reporting, safety, and human oversight.