Comparative Performance of Anthropic Claude and OpenAI GPT Models in Basic Radiological Imaging Tasks

Publicly available artificial intelligence (AI) Vision Language Models (VLMs) are constantly improving. The advent of vision capabilities on these models could enhance radiology workflows. Evaluating their performance in radiological image interpretation is vital to their potential integration into practice.

This study aims to evaluate the proficiency and consistency of the publicly available VLMs, Anthropic's Claude and OpenAI's GPT, across multiple iterations in basic image interpretation tasks.

Subsets from publicly available datasets, ROCOv2 and MURAv1.1, were used to evaluate 6 VLMs. A system prompt and image were input into each model three times. The outputs were compared to the dataset captions to evaluate each model's accuracy in recognising the modality, anatomy, and detecting fractures on radiographs. The consistency of the output across iterations was also analysed.

Evaluation of the ROCOv2 dataset showed high accuracy in modality recognition, with some models achieving 100%. Anatomical recognition ranged between 61% and 85% accuracy across all models tested. On the MURAv1.1 dataset, Claude-3.5-Sonnet had the highest anatomical recognition with 57% accuracy, while GPT-4o had the best fracture detection with 62% accuracy. Claude-3.5-Sonnet was the most consistent model, with 83% and 92% consistency in anatomy and fracture detection, respectively.

Given Claude and GPT's current accuracy and reliability, the integration of these models into clinical settings is not yet feasible. This study highlights the need for ongoing development and establishment of standardised testing techniques to ensure these models achieve reliable performance.