Evaluating the Citation Accuracy of ChatGPT-5 Using the American Academy of Orthopaedic Surgeons Clinical Practice Guidelines

Background: Large language models (LLMs) have demonstrated strong performance in generating medical responses; however, concerns persist regarding the accuracy of citations for these responses. Prior work has shown that earlier models frequently fabricate or misattribute references when queried on American Academy of Orthopaedic Surgeons (AAOS) Clinical Practice Guidelines (CPGs). With rapid advancements in model development, newer-generation LLMs may demonstrate improved citation accuracy and reliability. This study evaluates the citation accuracy of ChatGPT-5 in response to AAOS CPG-based queries. Methods: A computer-based observational study evaluating the citation accuracy of ChatGPT-5 (December 2025) was conducted from February to March 2026. Seventy recommendations from four AAOS CPGs were converted into standardized prompts, and ChatGPT was asked to generate a list of references to support its claims in response to these prompts. Five independent graders evaluated responses for citation accuracy. Citation elements assessed included title, authorship, journal, year, volume, pages, and PubMed identifier (PMID). Hallucinated references were defined as nonexistent or non-indexed studies. Results: A total of 350 queries generated 2,736 references, of which 7.13% were fabricated. Citation inaccuracies were most frequent for PMID (37.28%), title (30.70%), and pages (27.49%). Overall, 49.34% of citations were bibliographically accurate, and complete accuracy within a query occurred in 8.00% of cases. Conclusions: Citation errors and fabricated references persist in ChatGPT-5. Independent verification of generated references therefore remains necessary.