人工智能在心血管疾病预防中的应用：是否已准备好投入使用？
Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?
机构信息
Department of Medicine, Stanford University, Stanford, California, USA.
Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
出版信息
Curr Atheroscler Rep. 2024 Jul;26(7):263-272. doi: 10.1007/s11883-024-01210-w. Epub 2024 May 23.
PURPOSE OF REVIEW
This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.
RECENT FINDINGS
AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
目的综述
本综述通过分析非结构化临床数据和患者生成的数据，评估人工智能（AI）如何增强动脉粥样硬化性心血管疾病（ASCVD）风险评估、进行机会性筛查以及提高对指南的依从性。此外，还讨论了在预防心脏病学中整合 AI 的策略。
最近的发现
与传统风险评分相比，AI 模型在个性化 ASCVD 风险评估方面表现出卓越的性能。这些模型现在支持在各种成像方式中自动检测 ASCVD 风险标志物，包括冠状动脉钙（CAC），这些成像方式包括专门的心电图门控 CT 扫描、胸部 X 射线、乳房 X 光检查、冠状动脉造影和非门控胸部 CT 扫描。此外，大型语言模型 (LLM) 管道在识别和解决 ASCVD 预防保健中的差距和差异方面非常有效，并且还可以增强患者教育。AI 应用在预防和管理 ASCVD 方面非常有价值，并且在经过良好监管的迭代临床路径内实施的情况下，已经准备好临床使用。
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