抄録
This review provides an up-to-date overview of recent advances in artificial intelligence (AI) applied to cardiovascular medicine, spanning technological innovations, clinical applications, and broader societal implications. Over the past several years, deep learning methods have been increasingly adopted across multiple diagnostic modalities, including electrocardiography, echocardiography, coronary computed tomography angiography (CCTA), and intravascular ultrasound (IVUS), resulting in measurable improvements in diagnostic accuracy and workflow efficiency. In CCTA, AI facilitates automated detection of coronary stenosis and plaque burden. Echocardiography enables image segmentation and quantification of the left ventricular ejection fraction, while in IVUS, it is primarily applied to vessel structure analysis and lumen segmentation, and has also been used in conjunction with high-sensitivity troponin I to develop models capable of early ischemia assessment, offering the potential to rule out myocardial infarction from a single blood draw and to provide long-term risk estimates. More recently, domain-specific large language models, such as Med-PaLM, Gemini, and Articulate Medical Intelligence Explorer, have demonstrated diagnostic capabilities approaching physician-level performance in selected tasks. These systems are beginning to show promise for real-world clinical use by integrating multimodal data and providing interactive decision-making support. Nonetheless, several unresolved challenges persist, including the differences in cognitive reasoning between AI and clinicians, limited explainability, and complex regulatory and ethical considerations. Ongoing efforts, including randomized controlled trials (e.g., PROTEUS and RAPIDxAI) as well as the development of inclusive and sustainable digital health frameworks, aim to support the safe integration of AI into routine cardiovascular care. This review synthesizes current progress from technological, clinical, and implementation standpoints and explores the conditions under which AI can serve not as a replacement for clinicians, but as a collaborative partner in patient care.
