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Utilizing GPT-4o with context-based approaches for improved query of ACR RADS guidelines

Year: 2026

Type: article

Abstract: RATIONALE AND OBJECTIVES: Context-based large language models (LLM) with features like PDF integration and retrieval-augmented generation (RAG) may enhance accuracy on medical guideline queries. MATERIALS AND METHODS: 200 representative questions were drafted across five major guidelines of the American College of Radiology (ACR) Reporting and Data Systems (RADS), with 40 questions each for CAD-RADS, BI-RADS, LI-RADS, PI-RADS, and Lung-RADS. Prompts were presented once to three GPT-4o-based models: without additional context, with PDF attachment of the guideline, and using web-based retrieval-augmented generation (RAG). RESULTS: GPT-4o with PDF support answered 90% of questions correctly (180/200), outperforming both GPT-4o with RAG (83%, 165/200; P < 0.001) and GPT-4o without additional context (70%, 140/200; P < 0.001). The average response time was similar across all models: GPT-4o with PDF (13.6 ± 6.8 sec), GPT-4o with RAG (13.5 ± 3.5 sec), and GPT-4o without context (13.6 ± 6.3 sec; P = 0.97). CONCLUSION: Incorporating contextual information such as guideline documents substantially improves the accuracy of GPT-4o in answering ACR RADS-related questions.

Source: European Journal of Radiology

DOI: 10.1016/j.ejrad.2026.112807