Evaluating Large Language Model Adherence to AAOS Knee Osteoarthritis Guidelines: A Comparative Study of ChatGPT and NotebookLM

Purpose This study evaluated how closely large language models (LLMs), specifically ChatGPT (OpenAI) and NotebookLM (Google), adhere to orthopedic guidelines. The objective was to determine whether AI-generated reasoning aligns with the 2021–2022 American Academy of Orthopaedic Surgeons (AAOS) clinical practice guidelines for knee osteoarthritis (OA). Methods A mixed-methods design combined quantitative concordance scoring with qualitative content analysis. Thirty-three decision points covering non-arthroplasty and surgical management were derived from AAOS guidelines. Structured Population–Intervention–Comparison–Outcome (PICO) prompts were presented to each model. Two orthopedic surgeons independently rated all outputs using a four-domain rubric assessing accuracy, evidence reasoning, additional information, and knowledge integration (0–4 scale). Concordance was classified as full (4), partial (3), or discordant (≤ 2), with disagreements resolved through consensus. Inter-rater reliability was almost perfect (weighted κ = 0.87). Results ChatGPT achieved a mean composite score of 3.67 ± 0.92, and NotebookLM 3.55 ± 0.87, with no significant difference between models (p = 0.18). Full concordance with AAOS recommendations occurred in 84.8% of ChatGPT responses and 75.8% of NotebookLM responses. Both models performed consistently in high-evidence domains such as NSAID therapy, tranexamic acid use, and weight-loss counseling. Variability increased in limited-evidence or technology-driven areas. Partial concordance reflected the omission of evidence qualifiers, while discordant responses involved overstated or speculative interpretations. Conclusion Both LLMs demonstrated strong alignment with evidence-based orthopedic reasoning. ChatGPT showed slightly higher fidelity to recommendation strength, whereas NotebookLM provided broader contextual interpretation. Structured, guideline-oriented prompting may enhance AI reasoning consistency and support the potential role of LLMs as complementary tools for evidence translation and orthopedic education.