发表机构
Nagoya University; Graduate School of Informatics, Nagoya University（名古屋大学; 名古屋大学信息学研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文概述生成式AI相关的扩散模型、LLMs及基础模型，介绍其在医疗辅助中的应用，探讨基础模型的构建方法与医疗应用，并研究利用国家资源开发医疗辅助AI的路径。
AI 中文摘要
近年来，生成式AI已引发公众广泛关注，其应用正快速拓展至众多领域：从文本摘要、创意生成、源代码生成等创意任务，到诊断报告生成与摘要等医疗辅助任务的优化，AI如今已深度介入诸多领域。当前AI应用的广度，与生成式AI获广泛认可前的情况截然不同。具代表性的生成式AI服务包括用于图像生成的DALL-E 3（美国加利福尼亚州OpenAI公司）、Stable Diffusion（英国伦敦Stability AI公司），以及用于文本生成的ChatGPT（美国加利福尼亚州OpenAI公司）、Gemini（美国加利福尼亚州谷歌公司）。生成式AI的兴起得益于深度学习模型的进步，以及基于缩放定律对数据、模型、计算资源的扩容；此外，在大规模数据集上训练、具备适用于各类下游任务通用知识的基础模型的出现，正开创AI发展的新范式。生成式AI与基础模型带来的这些变革，也深刻影响着医学图像处理，从根本上改变了医疗领域AI开发的框架。本文概述了图像生成AI所用的扩散模型、文本生成AI所用的大语言模型（LLMs），并介绍了它们在医疗辅助中的应用；还探讨了与生成式AI一同受关注的基础模型，包括其构建方法及在医疗领域的应用；最后，本文探究了如何充分利用国家层面的数据与计算资源，开发用于医疗辅助的基础模型及高性能AI。
英文摘要
In recent years, generative AI has attracted significant public attention, and its use has been rapidly expanding across a wide range of domains. From creative tasks such as text summarization, idea generation, and source code generation, to the streamlining of medical support tasks like diagnostic report generation and summarization, AI is now deeply involved in many areas. Today's breadth of AI applications is clearly distinct from what was seen before generative AI gained widespread recognition. Representative generative AI services include DALL-E 3 (OpenAI, California, USA) and Stable Diffusion (Stability AI, London, England, UK) for image generation, ChatGPT (OpenAI, California, USA), and Gemini (Google, California, USA) for text generation. The rise of generative AI has been influenced by advances in deep learning models and the scaling up of data, models, and computational resources based on the scaling laws. Moreover, the emergence of foundation models, which are trained on large-scale datasets and possess general-purpose knowledge applicable to various downstream tasks, is creating a new paradigm in AI development. These shifts brought about by generative AI and foundation models also profoundly impact medical image processing, fundamentally changing the framework for AI development in healthcare. This paper provides an overview of diffusion models used in image generation AI and large language models (LLMs) used in text generation AI, and introduces their applications in medical support. This paper also discusses foundation models, which are gaining attention alongside generative AI, including their construction methods and applications in the medical field. Finally, the paper explores how to develop foundation models and high-performance AI for medical support by fully utilizing national data and computational resources.
CommentsReview Article
Journal refRadiological Physics and Technology, vol.18, pp.937-948, 2025
DOI：10.1007/s12194-025-00968-1
