Diagnostic accuracy of GPT-4 on common clinical scenarios and challenging cases

Abstract

Introduction

Large language models (LLMs) have a high diagnostic accuracy when they evaluate previously published clinical cases.

Methods

We compared the accuracy of GPT-4's differential diagnoses for previously unpublished challenging case scenarios with the diagnostic accuracy for previously published cases.

Results

For a set of previously unpublished challenging clinical cases, GPT-4 achieved 61.1% correct in its top 6 diagnoses versus the previously reported 49.1% for physicians. For a set of 45 clinical vignettes of more common clinical scenarios, GPT-4 included the correct diagnosis in its top 3 diagnoses 100% of the time versus the previously reported 84.3% for physicians.

Conclusions

GPT-4 performs at a level at least as good as, if not better than, that of experienced physicians on highly challenging cases in internal medicine. The extraordinary performance of GPT-4 on diagnosing common clinical scenarios could be explained in part by the fact that these cases were previously published and may have been included in the training dataset for this LLM.