A cross-sectional study on ChatGPT’s alignment with clinical practice guidelines in musculoskeletal rehabilitation
Abstract
AI models like ChatGPT have the potential to support musculoskeletal rehabilitation by providing clinical insights. However, their alignment with evidence-based guidelines needs evaluation before integration into physiotherapy practice. To evaluate the performance of ChatGPT (GPT-4 model) in generating responses to musculoskeletal rehabilitation queries by comparing its recommendations with evidence-based clinical practice guidelines (CPGs). This study was designed as a cross-sectional observational study. Twenty questions covering disease information, assessment, and rehabilitation were developed by two experienced physiotherapists specializing in musculoskeletal disorders. The questions were distributed across three anatomical regions: upper extremity (7 questions), lower extremity (9 questions), and spine (4 questions). ChatGPT’s responses were obtained and evaluated independently by two raters using a 5-point Likert scale assessing relevance, accuracy, clarity, completeness, and consistency. Weighted kappa values were calculated to assess inter-rater agreement and consistency within each category. ChatGPT’s responses received the highest average score for clarity (4.85), followed by accuracy (4.62), relevance (4.50), and completeness (4.20). Consistency received the lowest score (3.85). The highest agreement (weighted kappa = 0.90) was observed in the disease information category, whereas rehabilitation displayed relatively lower agreement (weighted kappa = 0.56).…
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