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Can generative artificial intelligence pass the orthopaedic board examination?

Ula N Isleem1, Bashar Zaidat1, Renee Ren1

  • 1Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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Large Language Models like ChatGPT can answer 60.8% of Orthopaedic In-Training Examination questions. While useful for resident education, careful review of ChatGPT

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Area of Science:

  • Artificial Intelligence in Medical Education
  • Orthopaedic Surgery Training
  • Large Language Models (LLMs)

Background:

  • The Orthopaedic In-Training Examination (OITE) assesses resident knowledge and identifies those at risk of failing the American Board of Orthopaedic Surgery (ABOS) examination.
  • Optimal preparation strategies for the OITE are continually explored, with potential roles for Large Language Models (LLMs).
  • ChatGPT, an LLM, demonstrates an ability to provide accurate, detailed answers, suggesting its utility in medical education.

Purpose of the Study:

  • To evaluate the performance of ChatGPT on Orthopaedic In-Training Examinations.
  • To use Self-Assessment Exams (SAE) from the American Academy of Orthopaedic Surgeons (AAOS) database and approved literature as a proxy for the Orthopaedic Board Examination.

Main Methods:

  • 301 SAE questions from the AAOS database and literature were input into ChatGPT.
  • Each question was presented in a new chat session.
  • Answers were analyzed, categorized, and compared to the correct answers from OITE and SAE exams.

Main Results:

  • ChatGPT correctly answered 183 out of 301 questions (60.8%).
  • Highest correct answer rates were in basic science (81%), oncology (72.7%), shoulder and elbow (71.9%), and sports (71.4%).
  • Performance varied by question type: management (54.7%), diagnosis (71.7%), and knowledge recall (60.7%).

Conclusions:

  • ChatGPT shows potential in providing accurate answers for board-style orthopaedic questions.
  • Its current utility in clinical education is limited but evolving, requiring careful analysis of its reasoning for accuracy and clinical validity.