Assessment of large language models in musculoskeletal radiological anatomy: A comparative study with radiologists.

Abstract

Objectives: This study aims to evaluate the diagnostic performance of large language models (LLMs) in musculoskeletal radiological anatomy and to compare their accuracy with radiologists of varying experience levels.

Patients and methods: Between May 16, 2025 and June 12, 2025, a total of 175 multiple-choice questions (82 image-based, 93 text-only) were retrieved from Radiopaedia's open-access database. Questions were classified by anatomical region and imaging modality. Three LLMs, ChatGPT-4o (OpenAI), Claude 3.7 Sonnet (Anthropic), and Grok 3 (×AI), were assessed in a zero-shot setting. Their responses were compared to those of an attending musculoskeletal radiologist and two residents (senior and junior). Accuracy rates were calculated and statistically compared.

Results: The attending radiologist achieved the highest overall accuracy (79.4%), followed by the senior (72.6%) and junior resident (66.9%). Among LLMs, ChatGPT-4o performed best overall (69.7%), particularly in text-based questions (88.2%). All LLMs outperformed radiologists in text-based questions but underperformed in image-based ones. The attending radiologist significantly outperformed all LLMs in image interpretation (p<0.001). Variations in performance were also noted across anatomical regions and imaging modalities, with some LLMs exceeding radiologists in specific domains such as spinal or shoulder anatomy.

Conclusion: While LLMs, particularly ChatGPT-4o, show strong performance in text-based anatomical questions, their accuracy in image-based musculoskeletal radiology remains limited compared to human radiologists. These findings suggest that LLMs can serve as supplementary tools in education but require further optimization, particularly for visual interpretation tasks, before clinical implementation.