Parsa Shyon, Shah Priyansh, Doijad Ritu, Rodriguez Fatima
Department of Internal Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Department of Internal Medicine, Jacobi Hospital/Albert Einstein College of Medicine, New York City, NY, USA.
Curr Cardiol Rep. 2025 Feb 1;27(1):44. doi: 10.1007/s11886-025-02203-0.
This review discusses the transformative potential of artificial intelligence (AI) in ischemic heart disease (IHD) prevention. It explores advancements of AI in predictive modeling, biomarker discovery, and cardiovascular imaging. Finally, considerations for clinical integration of AI into preventive cardiology workflows are reviewed.
AI-driven tools, including machine learning (ML) models, have greatly enhanced IHD risk prediction by integrating multimodal data from clinical sources, patient-generated inputs, biomarkers, and imaging. Applications in these various data sources have demonstrated superior diagnostic accuracy compared to traditional methods. However, ensuring algorithm fairness, mitigating biases, enhancing explainability, and addressing ethical concerns remain critical for successful deployment. Emerging technologies like federated learning and explainable AI are fostering more robust, scalable, and equitable adoption. AI holds promise in reshaping preventive cardiology workflows, offering more precise risk assessment and personalized care. Addressing barriers related to equity, transparency, and stakeholder engagement is key for seamless clinical integration and sustainable, lasting improvements in cardiovascular care.
本综述探讨人工智能（AI）在缺血性心脏病（IHD）预防方面的变革潜力。它探讨了AI在预测建模、生物标志物发现和心血管成像方面的进展。最后，回顾了将AI临床整合到预防心脏病学工作流程中的注意事项。
包括机器学习（ML）模型在内的AI驱动工具，通过整合来自临床来源、患者生成的输入、生物标志物和成像的多模态数据，极大地提高了IHD风险预测能力。与传统方法相比，在这些各种数据源中的应用已显示出更高的诊断准确性。然而，确保算法公平性、减轻偏差、增强可解释性以及解决伦理问题对于成功部署仍然至关重要。联邦学习和可解释AI等新兴技术正在促进更强大、可扩展和公平的应用。AI有望重塑预防心脏病学工作流程，提供更精确的风险评估和个性化护理。解决与公平性、透明度和利益相关者参与相关的障碍是实现无缝临床整合以及心血管护理可持续、持久改善的关键。
