  Recent studies indicate that Generative Pre-trained Transformer 4 with Vision
(GPT-4V) outperforms human physicians in medical challenge tasks. However,
these evaluations primarily focused on the accuracy of multi-choice questions
alone. Our study extends the current scope by conducting a comprehensive
analysis of GPT-4V's rationales of image comprehension, recall of medical
knowledge, and step-by-step multimodal reasoning when solving New England
Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test
the knowledge and diagnostic capabilities of medical professionals. Evaluation
results confirmed that GPT-4V performs comparatively to human physicians
regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in
cases where physicians incorrectly answer, with over 78% accuracy. However, we
discovered that GPT-4V frequently presents flawed rationales in cases where it
makes the correct final choices (35.5%), most prominent in image comprehension
(27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our
findings emphasize the necessity for further in-depth evaluations of its
rationales before integrating such multimodal AI models into clinical
workflows.
