Performance of GPT-4 on the American College of Radiology In-Service Examination

Abstract

ABSTRACT Objectives No study has evaluated the ability of ChatGPT-4 to answer image-rich diagnostic radiology board exam questions or assessed for model drift in GPT-4’s image interpretation abilities. In our study we evaluate GPT-4’s performance on the American College of Radiology (ACR) 2022 Diagnostic Radiology In-Training Examination (DXIT). Methods Questions were sequentially input into GPT-4 with a standardized prompt. Each answer was recorded and overall accuracy was calculated, as was logic-adjusted accuracy, and accuracy on image-based questions. This experiment was repeated several months later to assess for model drift. Results GPT-4 achieved 58.5% overall accuracy, lower than the PGY-3 average (61.9%) but higher than the PGY-2 average (52.8%). Adjusted accuracy was 52.8%. GPT-4 showed significantly higher (p = 0.012) confidence for correct answers (87.1%) compared to incorrect (84.0%). Performance on image-based questions was notably poorer (p < 0.001) at 45.4% compared to text-only questions (80.0%), with adjusted accuracy for image questions of 36.4%. When the questions were repeated, GPT-4 chose a different answer 25.5% of the time and there was a small but insignificant decrease in accuracy. Discussion GPT-4 performed between PGY-2 and PGY-3 levels on the 2022 DXIT, but significantly poorer on image-based questions, and with large variability in answer choices across time points. This study underscores the potential and risks of using minimally-prompted general AI models in interpreting radiologic images as a diagnostic tool. Implementers of general AI radiology systems should exercise caution given the possibility of spurious yet confident responses.

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