Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Hosen Kiat, Eric Rohren
{"title":"医学成像中文本到图像生成人工智能图像创建的适用性。","authors":"Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Hosen Kiat, Eric Rohren","doi":"10.2967/jnmt.124.268402","DOIUrl":null,"url":null,"abstract":"<p><p>The recent emergence of text-to-image generative artificial intelligence (AI) diffusion models such as DALL-E, Firefly, Stable Diffusion, and Midjourney has been touted with popular hype about the transformative potential in health care. This hype-driven, rapid assimilation comes with few professional guidelines and without regulatory oversight. Despite documented limitations, text-to-image generative AI creations have permeated nuclear medicine and medical imaging. Given the representation of medical imaging professions and potential dangers in misrepresentation and errors from both a reputation and community harm perspective, critical quality assurance of text-to-image generative AI creations is required. Here, tools for evaluating the quality and fitness for purpose of generative AI images in nuclear medicine and imaging are discussed. Generative AI text-to-image creation suffers quality limitations that are generally prohibitive of mainstream use in nuclear medicine and medical imaging. Text-to-image generative AI diffusion models should be used within a framework of critical quality assurance for quality and accuracy.</p>","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":"63-67"},"PeriodicalIF":1.0000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fitness for Purpose of Text-to-Image Generative Artificial Intelligence Image Creation in Medical Imaging.\",\"authors\":\"Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Hosen Kiat, Eric Rohren\",\"doi\":\"10.2967/jnmt.124.268402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The recent emergence of text-to-image generative artificial intelligence (AI) diffusion models such as DALL-E, Firefly, Stable Diffusion, and Midjourney has been touted with popular hype about the transformative potential in health care. This hype-driven, rapid assimilation comes with few professional guidelines and without regulatory oversight. Despite documented limitations, text-to-image generative AI creations have permeated nuclear medicine and medical imaging. Given the representation of medical imaging professions and potential dangers in misrepresentation and errors from both a reputation and community harm perspective, critical quality assurance of text-to-image generative AI creations is required. Here, tools for evaluating the quality and fitness for purpose of generative AI images in nuclear medicine and imaging are discussed. Generative AI text-to-image creation suffers quality limitations that are generally prohibitive of mainstream use in nuclear medicine and medical imaging. Text-to-image generative AI diffusion models should be used within a framework of critical quality assurance for quality and accuracy.</p>\",\"PeriodicalId\":16548,\"journal\":{\"name\":\"Journal of nuclear medicine technology\",\"volume\":\" \",\"pages\":\"63-67\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2025-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of nuclear medicine technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2967/jnmt.124.268402\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of nuclear medicine technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2967/jnmt.124.268402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Fitness for Purpose of Text-to-Image Generative Artificial Intelligence Image Creation in Medical Imaging.
The recent emergence of text-to-image generative artificial intelligence (AI) diffusion models such as DALL-E, Firefly, Stable Diffusion, and Midjourney has been touted with popular hype about the transformative potential in health care. This hype-driven, rapid assimilation comes with few professional guidelines and without regulatory oversight. Despite documented limitations, text-to-image generative AI creations have permeated nuclear medicine and medical imaging. Given the representation of medical imaging professions and potential dangers in misrepresentation and errors from both a reputation and community harm perspective, critical quality assurance of text-to-image generative AI creations is required. Here, tools for evaluating the quality and fitness for purpose of generative AI images in nuclear medicine and imaging are discussed. Generative AI text-to-image creation suffers quality limitations that are generally prohibitive of mainstream use in nuclear medicine and medical imaging. Text-to-image generative AI diffusion models should be used within a framework of critical quality assurance for quality and accuracy.