Improved accuracy and efficiency of guideline-based orthopedic disability assessment with a retrieval-augmented AI assistant (NotebookLM) in anonymized cases.

Background: /aim: Traditional orthopedic disability assessment relies on complex guidelines and is prone to evaluator error and inefficiency. This study evaluated whether a retrieval-augmented generation (RAG) AI assistant (NotebookLM) improves accuracy and efficiency in guideline-based impairment rating compared with manual consultation. Materials and methods: In this prospective comparative study, 50 anonymized case-based clinical scenarios representing common musculoskeletal impairments were developed. Four orthopedic specialists evaluated all scenarios using two methods: manual guideline consultation and NotebookLM-assisted assessment restricted to the same regulation. The starting method was randomized, with each evaluator assessing 25 scenarios per method. The primary outcome was accuracy against an expert reference standard; the secondary outcome was time per scenario. Results: AI-assisted evaluation achieved higher accuracy than manual consultation (91% vs 68%; absolute difference, 23 percentage points, 95% CI 12.3 to 33.7; χ Conclusion: A RAG-based AI assistant improved both accuracy and efficiency in orthopedic impairment rating under controlled conditions. These findings suggest meaningful clinical utility in structured, guideline-bound workflows, although further external validation in routine clinical practice is necessary before broader adoption can be recommended.