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

Background Publicly available artificial intelligence (AI) Visual Language Models (VLMs) are constantly improving. The advent of vision capabilities on these models could enhance workflows in radiology. Evaluating their performance in radiological image interpretation is vital to their potential integration into practice. Aim This study aims to evaluate the proficiency and consistency of the publicly available VLMs, Claude and GPT, across multiple iterations in basic image interpretation tasks. Method Subsets from publicly available datasets, ROCOv2 and MURAv1.1, were used to evaluate 6 VLMs. A system prompt and image were inputted into each model thrice. 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. Results 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. Conclusion Given Claude and GPT’s current accuracy and reliability, 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.