Nobuyuki Kagiyama, Márton Tokodi, Quincy A Hathaway, Rima Arnaout, Rhodri Davies, Damini Dey, Nicolas Duchateau, Alan G Fraser, Shinichi Goto, Ankush D Jamthikar, Carolyn S P Lam, Evangelos K Oikonomou, David Ouyang, Ambarish Pandey, Timothy J Poterucha, Zahra Raisi-Estabragh, Jordan B Strom, Qiang Zhang, Naveena Yanamala, Partho P Sengupta
{"title":"PRIME 2.0:心血管成像相关机器学习评估清单建议要求的更新。","authors":"Nobuyuki Kagiyama, Márton Tokodi, Quincy A Hathaway, Rima Arnaout, Rhodri Davies, Damini Dey, Nicolas Duchateau, Alan G Fraser, Shinichi Goto, Ankush D Jamthikar, Carolyn S P Lam, Evangelos K Oikonomou, David Ouyang, Ambarish Pandey, Timothy J Poterucha, Zahra Raisi-Estabragh, Jordan B Strom, Qiang Zhang, Naveena Yanamala, Partho P Sengupta","doi":"10.1016/j.jcmg.2025.08.004","DOIUrl":null,"url":null,"abstract":"<p><p>The PRIME (Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation) 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI. The updated checklist was developed through a modified Delphi process by an international panel of clinical and technical experts. In contrast to general AI reporting guidelines, it delivers detailed, practical recommendations on all critical aspects of AI research and builds upon the original 7-domain framework by incorporating cardiovascular imaging-specific complexities such as cardiac motion, imaging artifacts, and interobserver variability. By promoting transparency and rigor, PRIME 2.0 can serve as a vital resource for researchers, clinicians, peer reviewers, and journal editors working at the forefront of AI in cardiovascular imaging.</p>","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":15.2000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.\",\"authors\":\"Nobuyuki Kagiyama, Márton Tokodi, Quincy A Hathaway, Rima Arnaout, Rhodri Davies, Damini Dey, Nicolas Duchateau, Alan G Fraser, Shinichi Goto, Ankush D Jamthikar, Carolyn S P Lam, Evangelos K Oikonomou, David Ouyang, Ambarish Pandey, Timothy J Poterucha, Zahra Raisi-Estabragh, Jordan B Strom, Qiang Zhang, Naveena Yanamala, Partho P Sengupta\",\"doi\":\"10.1016/j.jcmg.2025.08.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The PRIME (Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation) 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI. The updated checklist was developed through a modified Delphi process by an international panel of clinical and technical experts. In contrast to general AI reporting guidelines, it delivers detailed, practical recommendations on all critical aspects of AI research and builds upon the original 7-domain framework by incorporating cardiovascular imaging-specific complexities such as cardiac motion, imaging artifacts, and interobserver variability. By promoting transparency and rigor, PRIME 2.0 can serve as a vital resource for researchers, clinicians, peer reviewers, and journal editors working at the forefront of AI in cardiovascular imaging.</p>\",\"PeriodicalId\":14767,\"journal\":{\"name\":\"JACC. Cardiovascular imaging\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":15.2000,\"publicationDate\":\"2025-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JACC. Cardiovascular imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jcmg.2025.08.004\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JACC. Cardiovascular imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jcmg.2025.08.004","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
PRIME 2.0检查表是一个更新的、特定领域的框架,旨在标准化人工智能(AI)在心血管成像中的应用的开发、评估和报告。这一更新特别回应了从传统机器学习到深度学习、大型语言模型和多模态生成人工智能的快速发展。更新的清单是由一个国际临床和技术专家小组通过改进的德尔菲程序制定的。与一般的人工智能报告指南相比,它对人工智能研究的所有关键方面提供了详细、实用的建议,并通过纳入心血管成像特定复杂性(如心脏运动、成像伪影和观察者之间的可变性),在原始的七域框架的基础上建立。通过提高透明度和严谨性,PRIME 2.0可以作为研究人员、临床医生、同行审稿人和期刊编辑在心血管成像人工智能领域工作的重要资源。
PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.
The PRIME (Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation) 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI. The updated checklist was developed through a modified Delphi process by an international panel of clinical and technical experts. In contrast to general AI reporting guidelines, it delivers detailed, practical recommendations on all critical aspects of AI research and builds upon the original 7-domain framework by incorporating cardiovascular imaging-specific complexities such as cardiac motion, imaging artifacts, and interobserver variability. By promoting transparency and rigor, PRIME 2.0 can serve as a vital resource for researchers, clinicians, peer reviewers, and journal editors working at the forefront of AI in cardiovascular imaging.
期刊介绍:
JACC: Cardiovascular Imaging, part of the prestigious Journal of the American College of Cardiology (JACC) family, offers readers a comprehensive perspective on all aspects of cardiovascular imaging. This specialist journal covers original clinical research on both non-invasive and invasive imaging techniques, including echocardiography, CT, CMR, nuclear, optical imaging, and cine-angiography.
JACC. Cardiovascular imaging highlights advances in basic science and molecular imaging that are expected to significantly impact clinical practice in the next decade. This influence encompasses improvements in diagnostic performance, enhanced understanding of the pathogenetic basis of diseases, and advancements in therapy.
In addition to cutting-edge research,the content of JACC: Cardiovascular Imaging emphasizes practical aspects for the practicing cardiologist, including advocacy and practice management.The journal also features state-of-the-art reviews, ensuring a well-rounded and insightful resource for professionals in the field of cardiovascular imaging.