Li Xiang, Zhao Lin, Zhang Lu, Wu Zihao, Liu Zhengliang, Jiang Hanqi, Cao Chao, Xu Shaochen, Li Yiwei, Dai Haixing, Yuan Yixuan, Liu Jun, Li Gang, Zhu Dajiang, Yan Pingkun, Li Quanzheng, Liu Wei, Liu Tianming, Shen Dinggang
IEEE Rev Biomed Eng. 2025;18:113-129. doi: 10.1109/RBME.2024.3493775. Epub 2025 Jan 28.
Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks. Yet, when applied directly to specialized domains like medical imaging, which require in-depth expertise, these models face notable challenges arising from the medical field's inherent complexities and unique characteristics. In this review, we delve into the potential applications of AGI models in medical imaging and healthcare, with a primary focus on LLMs, Large Vision Models, and Large Multimodal Models. We provide a thorough overview of the key features and enabling techniques of LLMs and AGI, and further examine the roadmaps guiding the evolution and implementation of AGI models in the medical sector, summarizing their present applications, potentialities, and associated challenges. In addition, we highlight potential future research directions, offering a holistic view on upcoming ventures. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.
包括ChatGPT/GPT-4等大语言模型（LLM）在内的大规模通用人工智能（AGI）模型，在各种通用领域任务中取得了前所未有的成功。然而，当直接应用于医学成像等需要深入专业知识的专业领域时，这些模型面临着医学领域固有复杂性和独特特征所带来的显著挑战。在本综述中，我们深入探讨AGI模型在医学成像和医疗保健中的潜在应用，主要关注大语言模型、大视觉模型和大模态模型。我们全面概述了大语言模型和AGI的关键特性和支持技术，并进一步研究指导AGI模型在医疗领域发展和实施的路线图，总结它们目前的应用、潜力和相关挑战。此外，我们突出了潜在的未来研究方向，对即将开展的项目提供全面的观点。这篇全面的综述旨在深入了解AGI在医学成像、医疗保健及其他领域的未来影响。
