Pantelidis Panteleimon, Dilaveris Polychronis, Ruipérez-Campillo Samuel, Goliopoulou Athina, Giannakodimos Alexios, Theofilis Panagiotis, De Lucia Raffaele, Katsarou Ourania, Zisimos Konstantinos, Kalogeras Konstantinos, Oikonomou Evangelos, Siasos Gerasimos
3rd Department of Cardiology, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Department of Computer and Systems Sciences, Stockholm University, 16455 Stockholm, Sweden.
Biomedicines. 2025 Apr 23;13(5):1019. doi: 10.3390/biomedicines13051019.
Artificial intelligence (AI) is transforming cardiovascular medicine by enabling the analysis of high-dimensional biomedical data with unprecedented precision. Initially employed to automate human tasks such as electrocardiogram (ECG) interpretation and imaging segmentation, AI's true potential lies in uncovering hidden disease data patterns, predicting long-term cardiovascular risk, and personalizing treatments. Unlike human cognition, which excels in certain tasks but is limited by memory and processing constraints, AI integrates multimodal data sources-including ECG, echocardiography, cardiac magnetic resonance (CMR) imaging, genomics, and wearable sensor data-to generate novel clinical insights. AI models have demonstrated remarkable success in early dis-ease detection, such as predicting heart failure from standard ECGs before symptom on-set, distinguishing genetic cardiomyopathies, and forecasting arrhythmic events. However, several challenges persist, including AI's lack of contextual understanding in most of these tasks, its "black-box" nature, and biases in training datasets that may contribute to disparities in healthcare delivery. Ethical considerations and regulatory frameworks are evolving, with governing bodies establishing guidelines for AI-driven medical applications. To fully harness the potential of AI, interdisciplinary collaboration among clinicians, data scientists, and engineers is essential, alongside open science initiatives to promote data accessibility and reproducibility. Future AI models must go beyond task automation, focusing instead on augmenting human expertise to enable proactive, precision-driven cardiovascular care. By embracing AI's computational strengths while addressing its limitations, cardiology is poised to enter an era of transformative innovation beyond traditional diagnostic and therapeutic paradigms.
人工智能（AI）正在以前所未有的精度分析高维生物医学数据，从而改变心血管医学。人工智能最初用于实现心电图（ECG）解读和成像分割等人类任务的自动化，其真正潜力在于揭示隐藏的疾病数据模式、预测长期心血管风险以及实现治疗个性化。与在某些任务中表现出色但受记忆和处理能力限制的人类认知不同，人工智能整合了多模态数据源，包括心电图、超声心动图、心脏磁共振（CMR）成像、基因组学和可穿戴传感器数据，以产生新的临床见解。人工智能模型在疾病早期检测方面已取得显著成功，例如在症状出现前从标准心电图预测心力衰竭、区分遗传性心肌病以及预测心律失常事件。然而，仍存在一些挑战，包括人工智能在大多数这些任务中缺乏情境理解、其“黑箱”性质以及训练数据集中的偏差，这些偏差可能导致医疗服务的差异。伦理考量和监管框架正在不断发展，管理机构正在为人工智能驱动的医疗应用制定指导方针。为了充分发挥人工智能的潜力，临床医生、数据科学家和工程师之间的跨学科合作至关重要，同时还需要开展开放科学倡议，以促进数据的可获取性和可重复性。未来的人工智能模型必须超越任务自动化，转而专注于增强人类专业知识，以实现主动、精准驱动的心血管护理。通过发挥人工智能的计算优势并解决其局限性，心脏病学有望进入一个超越传统诊断和治疗范式的变革性创新时代。
