Bernstein Brett S, Streather Sona, O'Gallagher Kevin
School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK.
Department of Cardiology, King's College Hospital NHS Foundation Trust, London, UK.
Future Cardiol. 2025 Jul 3:1-7. doi: 10.1080/14796678.2025.2523155.
Heart Failure is a prevalent disease with significant impacts on morbidity and mortality. Heart failure patients have a large volume of healthcare data which is digitized and can be collated. Artificial intelligence (AI) can then be used to assess the data for underlying patterns. AI systems can be trained to analyze readily available data, such as ECGs and heart sounds, and assess likelihood of heart failure. AI can also risk stratify heart failure patients by analyzing available healthcare data. AI can allow rapid assignment of heart failure patients to specific groups via automated echo analysis, but can also provide information regarding novel imaging bio-markers that may be more useful than left ventricular ejection fraction, such as first phase ejection fraction. AI can be used to assess patients' suitability for existing drugs, whilst also enabling development of novel drugs for known or newly discovered drug targets. Heart Failure as a field, with its multi-modal data set and variability in outcomes, will greatly benefit from the expansion and improvement of AI technology over the next 20 years.
心力衰竭是一种普遍存在的疾病，对发病率和死亡率有重大影响。心力衰竭患者有大量已数字化且可整理的医疗保健数据。然后可以使用人工智能（AI）来评估数据中的潜在模式。人工智能系统可以经过训练来分析诸如心电图和心音等现成数据，并评估心力衰竭的可能性。人工智能还可以通过分析可用的医疗保健数据对心力衰竭患者进行风险分层。人工智能可以通过自动超声心动图分析将心力衰竭患者快速分配到特定组，但也可以提供有关可能比左心室射血分数更有用的新型成像生物标志物的信息，例如第一阶段射血分数。人工智能可用于评估患者对现有药物的适用性，同时也有助于针对已知或新发现的药物靶点开发新型药物。作为一个领域，心力衰竭因其多模态数据集和结果的变异性，将在未来20年从人工智能技术的扩展和改进中大大受益。
