Shen Yiqiu, Xu Yanqi, Ma Jiajian, Rui Wushuang, Zhao Chen, Heacock Laura, Huang Chenchan
New York University Langone Medical Center, New York, USA.
New York University, New York, USA.
Abdom Radiol (NY). 2025 Jun;50(6):2745-2757. doi: 10.1007/s00261-024-04708-8. Epub 2024 Dec 2.
Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides a comprehensive overview of their capabilities and potential impact on radiology. Unlike most existing literature reviews focusing solely on LLMs, this work examines both LLMs and MLLMs, highlighting their potential to support radiology workflows such as report generation, image interpretation, EHR summarization, differential diagnosis generation, and patient education. By streamlining these tasks, LLMs and MLLMs could reduce radiologist workload, improve diagnostic accuracy, support interdisciplinary collaboration, and ultimately enhance patient care. We also discuss key limitations, such as the limited capacity of current MLLMs to interpret 3D medical images and to integrate information from both image and text data, as well as the lack of effective evaluation methods. Ongoing efforts to address these challenges are introduced.
大语言模型（LLMs）和多模态大语言模型（MLLMs）代表了人工智能的前沿技术。本综述全面概述了它们的能力以及对放射学的潜在影响。与大多数仅专注于大语言模型的现有文献综述不同，本研究同时考察了大语言模型和多模态大语言模型，强调了它们在支持放射学工作流程方面的潜力，如报告生成、图像解读、电子健康记录总结、鉴别诊断生成以及患者教育。通过简化这些任务，大语言模型和多模态大语言模型可以减轻放射科医生的工作量，提高诊断准确性，支持跨学科协作，并最终提升患者护理水平。我们还讨论了关键局限性，例如当前多模态大语言模型在解读3D医学图像以及整合图像和文本数据信息方面的能力有限，以及缺乏有效的评估方法。文中介绍了为应对这些挑战正在进行的努力。
