Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports

• URL: http://arxiv.org/abs/2508.15845v2
• Date: Sat, 27 Sep 2025 16:19:12 GMT
• Title: Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports
• Authors: Chengbo Sun, Hui Yi Leong, Lei Li,
• Abstract summary: "Impression" section in radiology reports is a primary driver of radiologist burnout.<n>We propose a coarse-to-fine framework that leverages open-source large language models (LLMs) to automatically generate and personalize impressions from clinical findings.<n>We fine-tune LLaMA and Mistral models on a large dataset of reports from the University of Chicago Medicine.
• Score: 5.373905622325275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The manual creation of the "Impression" section in radiology reports is a primary driver of radiologist burnout. To address this challenge, we propose a coarse-to-fine framework that leverages open-source large language models (LLMs) to automatically generate and personalize impressions from clinical findings. The system first produces a draft impression and then refines it using machine learning and reinforcement learning from human feedback (RLHF) to align with individual radiologists' styles while ensuring factual accuracy. We fine-tune LLaMA and Mistral models on a large dataset of reports from the University of Chicago Medicine. Our approach is designed to significantly reduce administrative workload and improve reporting efficiency while maintaining high standards of clinical precision.

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