Transforming heart failure care through AI: Advances and future directions

Abstract

Heart failure (HF) is a condition in which the heart cannot adequately pump enough blood to the body to meet its needs. Typically, this is due to a physical or functional problem with the heart. Although the traditional diagnostic and treatment options for HF have been improved upon, the growing numbers of individuals with HF (primarily due to cardiovascular diseases, hypertension and myocardial disorders) necessitate more specific, timely and personalised approaches to managing patients with HF. Artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), will have a significant impact on the future of cardiac care. AI has demonstrated increased accuracy for HF diagnosis, risk prediction, tailored therapies and rapid decision-making in clinical practice. The review will first provide an overview of the traditional methods for HF diagnostics and then compare them to the AI-based methods discussed later in the review. As shown in the review, AI generally offers a more accurate prediction of HF and allows for more frequent assessments. Traditionally, HF diagnostic methods rely on established guidelines and decision trees. In contrast, AI-based diagnostic methods utilise large amounts of complex input data including electronic health records (EHRs), electrocardiogram (ECG), echocardiogram images, CT/MRI scan results and laboratory test markers. These complex inputs allow AI to detect HF earlier than traditional methods, to better predict the risk associated with HF and to support development of individualised patient care plans. The article includes both a comparative analysis of HF diagnostics using traditional methods versus AI-based methods and examples of various smart implantable devices, vascular/structural heart interventions, rhythm-specific treatments and circulatory assistive devices to demonstrate the positive contributions of automated systems to higher recovery rates and reduced clinical burdens. Therefore, AI could represent a revolutionary new paradigm for managing HF, allowing clinicians to detect HF earlier and more accurately classify the type of HF, as well as select treatments based on evidence. With continued advancements in AI technology, combined with greater availability of real-world clinical patient data, it is likely that the delivery of HF care will undergo a significant transformation over the course of the next few decades.