Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.

IF 4.4 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Ha Kyung Jung, Kiduk Kim, Ji Eun Park, Namkug Kim
{"title":"Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.","authors":"Ha Kyung Jung, Kiduk Kim, Ji Eun Park, Namkug Kim","doi":"10.3348/kjr.2024.0392","DOIUrl":null,"url":null,"abstract":"<p><p>Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotated imaging data, which facilitates multiple downstream deep-learning tasks. However, their evaluation methods and clinical utility have not been thoroughly reviewed. This article summarizes commonly used generative adversarial networks and diffusion models. In addition, it summarizes their utility in clinical tasks in the field of radiology, such as direct image utilization, lesion detection, segmentation, and diagnosis. This article aims to guide readers regarding radiology practice and research using image-generative AI by 1) reviewing basic theories of image-generative AI, 2) discussing the methods used to evaluate the generated images, 3) outlining the clinical and research utility of generated images, and 4) discussing the issue of hallucinations.</p>","PeriodicalId":17881,"journal":{"name":"Korean Journal of Radiology","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11524689/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Korean Journal of Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3348/kjr.2024.0392","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotated imaging data, which facilitates multiple downstream deep-learning tasks. However, their evaluation methods and clinical utility have not been thoroughly reviewed. This article summarizes commonly used generative adversarial networks and diffusion models. In addition, it summarizes their utility in clinical tasks in the field of radiology, such as direct image utilization, lesion detection, segmentation, and diagnosis. This article aims to guide readers regarding radiology practice and research using image-generative AI by 1) reviewing basic theories of image-generative AI, 2) discussing the methods used to evaluate the generated images, 3) outlining the clinical and research utility of generated images, and 4) discussing the issue of hallucinations.

放射学中基于图像的生成人工智能:全面更新。
图像生成人工智能(AI)已被应用于各种医疗领域的图像质量增强、领域转移和人工智能建模训练数据的扩充。图像生成式人工智能可以生成大量未标注的图像数据,从而为多种下游深度学习任务提供便利。然而,其评估方法和临床实用性尚未得到深入研究。本文总结了常用的生成对抗网络和扩散模型。此外,文章还总结了它们在放射学领域临床任务中的实用性,如直接利用图像、病变检测、分割和诊断。本文旨在通过 1) 回顾图像生成人工智能的基本理论,2) 讨论用于评估生成图像的方法,3) 概述生成图像的临床和研究用途,以及 4) 讨论幻觉问题,为读者使用图像生成人工智能进行放射学实践和研究提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信