AI-Generated Graduate Medical Education Content for Total Joint Arthroplasty: Comparing ChatGPT Against Orthopaedic Fellows

Background: Artificial intelligence (AI) in medicine has primarily focused on diagnosing and treating diseases and assisting in the development of academic scholarly work. This study aimed to evaluate a new use of AI in orthopaedics: content generation for professional medical education. Quality, accuracy, and time were compared between content created by ChatGPT and orthopaedic surgery clinical fellows. Methods: ChatGPT and 3 orthopaedic adult reconstruction fellows were tasked with creating educational summaries of 5 total joint arthroplasty-related topics. Responses were evaluated across 5 domains by 4 blinded reviewers from different institutions who are all current or former total joint arthroplasty fellowship directors or national arthroplasty board review course directors. Results: ChatGPT created better orthopaedic content than fellows when mean aggregate scores for all 5 topics and domains were compared (P ≤ .001). The only domain in which fellows outperformed ChatGPT was the integration of key points and references (P = .006). ChatGPT outperformed the fellows in response time, averaging 16.6 seconds vs the fellows' 94 minutes per prompt (P = .002). Conclusions: With its efficient and accurate content generation, the current findings underscore ChatGPT's potential as an adjunctive tool to enhance orthopaedic arthroplasty graduate medical education. Future studies are warranted to explore AI's role further and optimize its utility in augmenting the educational development of arthroplasty trainees.