Evaluating GPT-4o's Performance in the Official European Board of Radiology Exam: A Comprehensive Assessment.

Abstract

RATIONALE AND OBJECTIVES This study aims to evaluate the performance of generative pre-trained transformer (GPT)-4o in the complete official European Board of Radiology (EBR) exam, designed to assess radiology knowledge, skills, and competence. MATERIALS AND METHODS Questions based on text, image, or video and in the format of multiple choice, free-text reporting, or image annotation were uploaded into GPT-4o using standardized prompting. The results were compared to the average scores of radiologists taking the exam in real time. RESULTS In Part 1 (multiple response questions and short cases), GPT-4o outperformed both the radiologists' average scores and the maximum pass score (70.2% vs. 58.4% and 60%, respectively). In Part 2 (clinically oriented reasoning evaluation), the performance of GPT-4o was below both the radiologists' average scores and the minimum pass score (52.9% vs. 66.1% and 55%, respectively). The accuracy on questions involving ultrasound images was higher compared to other imaging modalities (accuracy rate, 87.5-100%). For video-based questions, the performance was 50.6%. The model achieved the highest accuracy on most likely diagnosis questions but showed lower accuracy in free-text reporting and direct anatomical assessment in images (100% vs. 31% and 28.6%, respectively). CONCLUSION The abilities of GPT-4o in the official EBR exam are particularly noteworthy. This study demonstrates the potential of large language models to assist radiologists in assessing and managing cases from diagnosis to treatment or follow-up recommendations, even with zero-shot prompting.