ArticleFrontiers in medicine2026
ChatGPT-generated rehabilitation programs in sports physiotherapy: an expert evaluation and a mixed-methods study of clinical applicability.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Individualized rehabilitation supports safe return to sport, yet clinical workload may limit personalized program design. This study evaluated ChatGPT-4.1-generated programs for five sports injury scenarios using a structured rubric scored by two independent sports physiotherapy experts and examined inter-rater reliability and clinical applicability. Methods: Mixed-methods design combining rubric scoring with deductive qualitative content analysis of expert commentary. Five cases covered muscle (hamstring strain), ligament (anterior cruciate ligament [ACL] reconstruction), tendon (rotator cuff tendinopathy), neural (lumbar disc herniation) and bone (clavicle fracture) injuries. Exactly one 6-week tabular program per case was generated with ChatGPT-4.1 from a standardized prompt in a fresh session with memory, cross-chat history and custom instructions disabled. Two sports physiotherapists (>20 years' experience) independently rated accuracy, exercise selection, progression and applicability on 1-5 scales, blinded to each other's ratings, the verbatim prompt (including the imposed six-week horizon), model identity and study aims, while retaining the clinical scenario. ICC[2,1] (two-way random-effects, absolute agreement) with 95% CIs was computed across the 20 case × criterion units; exact and within-one-point agreement, weighted Results: The overall mean of 40 ratings was 3.85 ± 1.21 (95% CI 3.48-4.22), reported alongside disaggregated case- and criterion-level values. Case 5 (clavicle fracture) scored highest (5.00 ± 0.00), Case 2 (ACL) lowest (1.88 ± 0.83, 95% CI 1.18-2.57); exercise selection scored highest (4.20 ± 1.32, 95% CI 3.26-5.14), clinical applicability lowest (3.60 ± 1.26, 95% CI 2.70-4.50). Inter-rater reliability was good (ICC[2,1] = 0.84, 95% CI 0.52-0.94; Conclusion: ChatGPT-4.1 generates plausible, structured programs for linear, protocol-based recovery (e.g., post-fracture), but performance declines markedly in complex, postoperative-staging-sensitive cases; it should serve as a clinician-supervised support tool, not an autonomous decision-maker. Because the six-week length was fixed across all scenarios, case complexity and prompt-scenario congruence are confounded. Findings rest on five programs from one prompting strategy and model, requiring confirmation in larger multi-prompt, multi-model studies.
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