In recent years, the field of radiology has increasingly harnessed the power of artificial intelligence (AI) to enhance diagnostic accuracy, streamline workflows, and improve patient care. Large language models (LLMs) have emerged as particularly promising tools, offering significant potential in assisting radiologists with report generation, clinical decision support, and patient communication. This paper presents an advanced radiology-focused large language model: MGH Radiology Llama. It is developed using the Llama 3 70B model, building upon previous domain-specific models like Radiology-GPT and Radiology-Llama2. Leveraging a unique and comprehensive dataset from Massachusetts General Hospital, comprising over 6.5 million de-identified medical reports across various imaging modalities, the model demonstrates significant improvements in generating accurate and clinically relevant radiology impressions given the corresponding findings. Our evaluation, incorporating both traditional metrics and a GPT-4-based assessment, highlights the enhanced performance of this work over general-purpose LLMs.

翻译：近年来，放射学领域日益利用人工智能（AI）的力量来提升诊断准确性、优化工作流程并改善患者护理。大型语言模型（LLM）已成为极具前景的工具，在协助放射科医生生成报告、提供临床决策支持和改善医患沟通方面展现出巨大潜力。本文提出一种先进的、专注于放射学的大型语言模型：MGH Radiology Llama。该模型基于Llama 3 70B架构开发，并在Radiology-GPT和Radiology-Llama2等先前的领域专用模型基础上构建。通过利用来自麻省总医院的独特且全面的数据集——包含超过650万份跨多种成像模式的去标识化医疗报告，该模型在根据相应检查所见生成准确且具有临床相关性的放射学印象方面表现出显著提升。我们的评估结合了传统指标和基于GPT-4的评估方法，突显了本工作相较于通用大型语言模型的性能优势。