Hu Mingzhe, Qian Joshua, Pan Shaoyan, Li Yuheng, Qiu Richard L J, Yang Xiaofeng
Department of Computer Science and Informatics, Emory University, Atlanta, GA, United States of America.
Department of Radiation Oncology, Winship Cancer Institute, School of Medicine, Emory University, Atlanta, GA, United States of America.
Phys Med Biol. 2024 May 3;69(10):10TR01. doi: 10.1088/1361-6560/ad387d.
This review paper aims to serve as a comprehensive guide and instructional resource for researchers seeking to effectively implement language models in medical imaging research. First, we presented the fundamental principles and evolution of language models, dedicating particular attention to large language models. We then reviewed the current literature on how language models are being used to improve medical imaging, emphasizing a range of applications such as image captioning, report generation, report classification, findings extraction, visual question response systems, interpretable diagnosis and so on. Notably, the capabilities of ChatGPT were spotlighted for researchers to explore its further applications. Furthermore, we covered the advantageous impacts of accurate and efficient language models in medical imaging analysis, such as the enhancement of clinical workflow efficiency, reduction of diagnostic errors, and assistance of clinicians in providing timely and accurate diagnoses. Overall, our goal is to have better integration of language models with medical imaging, thereby inspiring new ideas and innovations. It is our aspiration that this review can serve as a useful resource for researchers in this field, stimulating continued investigative and innovative pursuits of the application of language models in medical imaging.
这篇综述文章旨在为寻求在医学影像研究中有效应用语言模型的研究人员提供全面的指南和教学资源。首先，我们介绍了语言模型的基本原理和发展历程，特别关注了大语言模型。然后，我们回顾了当前关于语言模型如何用于改善医学影像的文献，重点介绍了一系列应用，如图像字幕生成、报告生成、报告分类、结果提取、视觉问答系统、可解释诊断等。值得注意的是，ChatGPT的功能受到了关注，以供研究人员探索其进一步的应用。此外，我们阐述了准确高效的语言模型在医学影像分析中的有利影响，如提高临床工作流程效率、减少诊断错误以及协助临床医生提供及时准确的诊断。总体而言，我们的目标是使语言模型与医学影像更好地整合，从而激发新的想法和创新。我们希望这篇综述能够成为该领域研究人员的有用资源，推动对语言模型在医学影像应用方面的持续研究和创新探索。
