Introduction: Post-stroke motor rehabilitation typically involves in-person therapy sessions in which clinicians prescribe tailored exercises for patients. However, access to in-person therapy is often limited, particularly for individuals in underserved and rural areas. While digital rehabilitation tools exist to bridge this gap, they frequently lack sufficient personalization considering the patient’s home environment and individual needs. To address this issue, we evaluated the feasibility of a novel AI-enabled augmented reality (AR) system that translates natural language into software using large language model (LLM) code generation at the point of care, allowing therapists to 1) design personalized, home-based exercises and 2) monitor exercise completion by patients in detail. Methods: In a prospective, single-arm proof-of-concept study, 20 therapists conducted simulated remote therapy sessions with a standardized patient with right upper extremity weakness using the AI-enabled AR system. Therapists prescribed personalized exercises with voice recordings and manual typings, which the LLM translated into software in the Scenic programming language. By using a commercial AR headset, the software provided instructions to the patient and independently monitored the completion of each exercise step, offering to therapists information to guide future exercise prescriptions. Results: The system successfully delivered 99.8% (95% CI: 98.6-100%) of the 398 instructions prescribed without errors or hallucinations. The accuracy of monitoring exercise completion was 88.4% (95% CI: 84.9-91.9%) when compared to the gold-standard evaluation by therapists. Therapists reported excellent usability (mean Likert 5-point score: 4.5 ± 0.5) and 75% indicated they would like to use the technology in clinical practice. For 90% of the therapists, the system did not have an added risk of injury compared to the current usual care with paper worksheets. Conclusions: In conclusion, our AR system can enable therapists to remotely create and deliver personalized rehabilitation exercises for stroke and other neurological conditions while monitoring completion. To our knowledge, this is the first study evaluating LLMs for real-time code generation to support clinicians in prescribing interventions in rehabilitation. This approach has the potential to expand access to individualized stroke rehabilitation beyond traditional in-person care.
Kim et al. (Thu,) studied this question.