面向医学专业人员的医学影像生成人工智能和大型语言模型最新入门读物。

IF 4.4 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Korean Journal of Radiology Pub Date : 2024-03-01 DOI:10.3348/kjr.2023.0818
Kiduk Kim, Kyungjin Cho, Ryoungwoo Jang, Sunggu Kyung, Soyoung Lee, Sungwon Ham, Edward Choi, Gil-Sun Hong, Namkug Kim
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引用次数: 0

摘要

由 OpenAI 开发的聊天机器人 Chat Generative Pre-trained Transformer(ChatGPT)的出现引起了人们对生成式人工智能(AI)模型在医学领域应用的兴趣。本综述总结了不同的生成式人工智能模型及其在医学领域的潜在应用,并探讨了自生成式人工智能模型问世以来,生成式对抗网络和扩散模型的演变情况。这些模型为放射学领域做出了宝贵的贡献。此外,本综述还探讨了合成数据在解决隐私问题、提高医疗领域数据多样性和质量方面的意义,此外还强调了反演在研究生成模型中的作用,并概述了复制这一过程的方法。我们概述了大型语言模型(如 GPT 和双向编码器表示法 (BERT)),重点介绍了其中的杰出代表,并讨论了放射学中涉及语言视觉模型的最新举措,包括用于生物医学的创新型大型语言和视觉助手 (LLaVa-Med),以说明其实际应用。这篇综合评论深入探讨了生成式人工智能模型在临床研究中的广泛应用,并强调了它们的变革潜力。
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Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

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来源期刊
Korean Journal of Radiology
Korean Journal of Radiology 医学-核医学
CiteScore
10.60
自引率
12.50%
发文量
141
审稿时长
1.3 months
期刊介绍: The inaugural issue of the Korean J Radiol came out in March 2000. Our journal aims to produce and propagate knowledge on radiologic imaging and related sciences. A unique feature of the articles published in the Journal will be their reflection of global trends in radiology combined with an East-Asian perspective. Geographic differences in disease prevalence will be reflected in the contents of papers, and this will serve to enrich our body of knowledge. World''s outstanding radiologists from many countries are serving as editorial board of our journal.
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