Jonathan W. Cunningham MD, MPH , William T. Abraham MD , Ankeet S. Bhatt MD, MBA, ScM , Jessilyn Dunn PhD , G. Michael Felker MD, MHS , Sneha S. Jain MD, MBA , Christopher J. Lindsell PhD , Matthew Mace BS , Trejeeve Martyn MD, MS , Rashmee U. Shah MD, MS , Geoffrey H. Tison MD, MPH , Tala Fakhouri PhD, MPH , Mitchell A. Psotka MD, PhD , Harlan Krumholz MD , Mona Fiuzat PharmD , Christopher M. O’Connor MD , Scott D. Solomon MD , Heart Failure Collaboratory
{"title":"Artificial Intelligence in Cardiovascular Clinical Trials","authors":"Jonathan W. Cunningham MD, MPH , William T. Abraham MD , Ankeet S. Bhatt MD, MBA, ScM , Jessilyn Dunn PhD , G. Michael Felker MD, MHS , Sneha S. Jain MD, MBA , Christopher J. Lindsell PhD , Matthew Mace BS , Trejeeve Martyn MD, MS , Rashmee U. Shah MD, MS , Geoffrey H. Tison MD, MPH , Tala Fakhouri PhD, MPH , Mitchell A. Psotka MD, PhD , Harlan Krumholz MD , Mona Fiuzat PharmD , Christopher M. O’Connor MD , Scott D. Solomon MD , Heart Failure Collaboratory","doi":"10.1016/j.jacc.2024.08.069","DOIUrl":null,"url":null,"abstract":"<div><div>Randomized clinical trials are the gold standard for establishing the efficacy and safety of cardiovascular therapies. However, current pivotal trials are expensive, lengthy, and insufficiently diverse. Emerging artificial intelligence (AI) technologies can potentially automate and streamline clinical trial operations. This review describes opportunities to integrate AI throughout a trial’s life cycle, including designing the trial, identifying eligible patients, obtaining informed consent, ascertaining physiological and clinical event outcomes, interpreting imaging, and analyzing or disseminating the results. Nevertheless, AI poses risks, including generating inaccurate results, amplifying biases against underrepresented groups, and violating patient privacy. Medical journals and regulators are developing new frameworks to evaluate AI research tools and the data they generate. Given the high-stakes role of randomized trials in medical decision making, AI must be integrated carefully and transparently to protect the validity of trial results.</div></div>","PeriodicalId":17187,"journal":{"name":"Journal of the American College of Cardiology","volume":"84 20","pages":"Pages 2051-2062"},"PeriodicalIF":21.7000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American College of Cardiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0735109724084572","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
Abstract
Randomized clinical trials are the gold standard for establishing the efficacy and safety of cardiovascular therapies. However, current pivotal trials are expensive, lengthy, and insufficiently diverse. Emerging artificial intelligence (AI) technologies can potentially automate and streamline clinical trial operations. This review describes opportunities to integrate AI throughout a trial’s life cycle, including designing the trial, identifying eligible patients, obtaining informed consent, ascertaining physiological and clinical event outcomes, interpreting imaging, and analyzing or disseminating the results. Nevertheless, AI poses risks, including generating inaccurate results, amplifying biases against underrepresented groups, and violating patient privacy. Medical journals and regulators are developing new frameworks to evaluate AI research tools and the data they generate. Given the high-stakes role of randomized trials in medical decision making, AI must be integrated carefully and transparently to protect the validity of trial results.
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