医学
杠杆(统计)
医疗保健
射血分数
病人护理
医学物理学
人工智能
重症监护医学
心脏病学
心力衰竭
计算机科学
经济增长
护理部
经济
作者
Pierre Elias,Sneha S. Jain,Timothy J. Poterucha,Michael Randazzo,Francisco López Jiménez,Rohan Khera,Marco Perez,David Ouyang,James P. Pirruccello,Michael Salerno,Andrew J. Einstein,Robert Avram,Geoffrey H. Tison,Girish N. Nadkarni,Vivek T. Natarajan,Emma Pierson,Ashley Beecy,Deepa Kumaraiah,Chris Haggerty,Jennifer N. Avari Silva,Thomas M. Maddox
标识
DOI:10.1016/j.jacc.2024.03.400
摘要
Recent AI advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitates rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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