GPT-4 as a Board-Certified Surgeon: A Pilot Study.

Area of Science:

• Artificial Intelligence in Medical Education
• Surgical Training Technologies
• Large Language Models (LLMs)

Background:

• Large language models (LLMs) show potential for revolutionizing surgical education.
• Skepticism regarding LLM accuracy and reliability hinders adoption in medical training.
• GPT-4's performance on multiple-choice questions is known, but its clinical judgment in oral examinations is less understood.

Purpose of the Study:

• To evaluate GPT-4's general surgery knowledge using mock written and oral board-style examinations.
• To identify areas for improvement in LLMs for surgical education and practice.
• To assess the reliability of GPT-4 in simulating high-stakes clinical decision-making scenarios.

Main Methods:

• GPT-4 answered 250 multiple-choice questions (MCQs) from the Surgical Council on Resident Education (SCORE) question bank.
• GPT-4 navigated 4 oral board scenarios based on Entrustable Professional Activities (EPA) topic list.
• Responses were independently assessed for accuracy by two former oral board examiners.

Main Results:

• GPT-4 achieved 78.8% accuracy on MCQs, indicating a 92% probability of passing the American Board of Surgery Qualifying Examination (ABS QE).
• GPT-4 committed critical failures in 75% of oral board scenarios (3 out of 4 cases).
• Key failure points included incorrect timing of interventions and inappropriate surgical recommendations.

Conclusions:

• GPT-4's high MCQ performance aligns with previous findings, but it demonstrated limitations in generating accurate long-form content for oral examinations.
• Significant improvements are required in LLM performance for complex clinical decision-making.
• Future research should focus on specialized datasets and advanced reinforcement learning to enhance LLM capabilities in surgical contexts.