ArticleJournal of hand surgery global online2024
Comparison of Artificial Intelligence to Resident Performance on Upper-Extremity Orthopaedic In-Training Examination Questions.
Article in Journal of hand surgery global online, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining the Role of Large Language Models in Orthopedics: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Performance of GPT-5 and Gemini 2.5 Pro on the Orthopaedic In-Training Examination.Orthopedic reviews · 2026Article
- Artificial intelligence models cannot yet replace experts in providing patient education for shoulder and elbow orthopaedic pathologies: a systematic review and meta-analysis.Annals of joint · 2026Article
- Artificial intelligence fails to outperform orthopaedic surgeons: A systematic review.Journal of experimental orthopaedics · 2025Review
- Impact of prompting on large language model performance: ChatGPT-4 performance on the 2023 hand surgery self-assessment examination.Journal of hand and microsurgery · 2025Article
- Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Currently, there is a paucity of prior investigations and studies examining applications for artificial intelligence (AI) in upper-extremity (UE) surgical education. The purpose of this investigation was to assess the performance of a novel AI tool (ChatGPT) on UE questions on the Orthopaedic In-Training Examination (OITE). We aimed to compare the performance of ChatGPT to the examination performance of hand surgery residents. Methods: We selected questions from the 2020-2022 OITEs that focused on both the hand and UE as well as the shoulder and elbow content domains. These questions were divided into two categories: those with text-only prompts (text-only questions) and those that included supplementary images or videos (media questions). Two authors (B.K.F. and G.S.M.) converted the accompanying media into text-based descriptions. Included questions were inputted into ChatGPT (version 3.5) to generate responses. Each OITE question was entered into ChatGPT three times: (1) open-ended response, which requested a free-text response; (2) multiple-choice responses without asking for justification; and (3) multiple-choice response with justification. We referred to the OITE scoring guide for each year in order to compare the percentage of correct AI responses to correct resident responses. Results: A total of 102 UE OITE questions were included; 59 were text-only questions, and 43 were media-based. ChatGPT correctly answered 46 (45%) of 102 questions using the Multiple Choice No Justification prompt requirement (42% for text-based and 44% for media questions). Compared to ChatGPT, postgraduate year 1 orthopaedic residents achieved an average score of 51% correct. Postgraduate year 5 residents answered 76% of the same questions correctly. Conclusions: ChatGPT answered fewer UE OITE questions correctly compared to hand surgery residents of all training levels. Clinical relevance: Further development of novel AI tools may be necessary if this technology is going to have a role in UE education.
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