AI提升心血管病诊断精度，但需验证数据质量才能落地。
Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research
- 用深度学习自动分析心电图、影像等医疗数据。
- 诊断准确率超越人工，提升诊疗效率。
- 适合医学研究者与临床工程师参考。
人工智能在心血管疾病研究中取得显著进展，尤其通过与计算机断层扫描（CT）、磁共振成像（MRI）、心电图（ECG）和超声（US）的融合应用。卷积神经网络与生成对抗网络等深度学习架构实现了对医学影像和生理信号的自动化分析，诊断准确率和工作流程效率均超越人类水平。然而仍存在输入数据准确性难以验证的问题，可能引发误诊。本文强调人工智能在精准诊断中的变革潜力，同时呼吁建立可靠的验证机制以保障临床可靠性。未来方向聚焦于多模态数据融合的混合模型与自适应算法，推动个性化心血管诊疗发展。
原文摘要 · Abstract (English)
Recent advancements in artificial intelligence (AI) have revolutionized cardiovascular medicine, particularly through integration with computed tomography (CT), magnetic resonance imaging (MRI), electrocardiography (ECG) and ultrasound (US). Deep learning architectures, including convolutional neural networks and generative adversarial networks, enable automated analysis of medical imaging and physiological signals, surpassing human capabilities in diagnostic accuracy and workflow efficiency. However, critical challenges persist, including the inability to validate input data accuracy, which may propagate diagnostic errors. This review highlights AI's transformative potential in precision diagnostics while underscoring the need for robust validation protocols to ensure clinical reliability. Future directions emphasize hybrid models integrating multimodal data and adaptive algorithms to refine personalized cardiovascular care.
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