# Diagnostic performance of large language models on the NEJM image challenge: a comparative study with human evaluators and the impact of prompt engineering

## TL;DR

This study compares the diagnostic accuracy of large language models and human evaluators on a medical image challenge, finding that the best model outperformed all human participants.

## Contribution

The study demonstrates that a leading multimodal LLM achieves higher diagnostic accuracy than medical students and physicians on a standardized image challenge.

## Key findings

- OpenAI o4-mini-high achieved 94% accuracy, surpassing human participants.
- Most model errors were due to diagnostic logic lapses, not input processing.
- Prompt engineering corrected over half of the model's initial errors.

## Abstract

Multimodal large language models (LLMs) that can interpret clinical text and images are emerging as potential decision-support tools, yet their accuracy on standardized cases and how it compares with human performance across different difficulty levels remains largely unclear. This study aimed to rigorously evaluate the performance of four leading LLMs on the 200-item New England Journal of Medicine (NEJM) Image Challenge.

We assessed OpenAI o4-mini-high, Claude 4 Opus, Gemini 2.5 Pro, and Qwen 3, and benchmarked the top model against three medical students (Years 5–7) and an internal-medicine attending physician under identical test conditions. Additionally, we characterized the dominant error types for OpenAI o4-mini-high and tested prompt engineering strategies for potential correction.

Our results suggest that OpenAI o4-mini-high achieved the highest overall accuracy of 94%. Its performance remained consistently high across easy, moderate, and difficult cases. The human accuracies in this cohort ranged from 38.5% for three medical students to 70.5% for an attending physician—all significantly lower than OpenAI o4-mini-high. An analysis of OpenAI o4-mini-high’s 12 errors revealed that most (83.3%) were outputs reflecting lapses in diagnostic logic rather than input processing. Notably, simple prompting techniques like chain-of-thought and few-shot learning corrected over half of these initial errors.

Within the context of this standardized challenge, a leading multimodal LLM delivered high diagnostic accuracy that surpassed the scores of both peer models and the recruited human participants. However, these results should be interpreted as evidence of pattern recognition capabilities rather than human-like clinical understanding. While further validation on real-world data is warranted, these findings support the potential utility of LLMs in educational and standardized settings, highlighting that most residual errors are due to logic gaps that can be partly mitigated by refined user prompting, emphasizing the importance of human-AI interaction for maximizing reliability.