Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models

Abstract

Question Can locally deployed open-weight large language models (LLMs) improve the readability and understandability of radiology reports for medical laypersons at a level comparable to closed-weight models? Findings LLMs significantly improved quantitative readability scores and qualitative ratings of layperson understandability; Llama-3-70B and GPT-4o showed comparable performance, and although the open-source models exhibited a higher error rate, they still performed well overall. Clinical relevance Open-weight LLMs provide a privacy-compliant way to generate a template for patient-friendly radiology reports suitable for real-world clinical use.

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