Heart failure (HF), a chronic and progressive syndrome with high morbidity and mortality worldwide, continues to impose a substantial burden on healthcare systems. Despite advances in guideline-directed medical therapy and device-based interventions, HF remains associated with frequent hospital readmissions, marked phenotypic heterogeneity, and variable therapeutic responses. In this context, artificial intelligence (AI) has emerged as a transformative tool with the potential to redefine HF management.
A recent review led by Professor Yi-Da Tang, M.D., Ph.D., from the Department of Cardiology and Institute of Vascular Medicine at Peking University Third Hospital, published online on March 5, 2026, in the Chinese Medical Journal , provides a comprehensive review of the latest progress in AI applications across the full spectrum of HF care. The authors highlight how AI enables a paradigm shift from conventional risk prediction toward precision treatment, from episodic assessment to continuous monitoring, and from population-based strategies to individualized prognostic modeling.
The review systematically summarizes the application of AI across multiple domains of HF management. By integrating large-scale structured electronic health records (EHRs), AI can identify high-risk individuals at earlier stages, uncover distinct HF subtypes, and support more accurate outcome prediction.
Beyond structured EHR data, AI is increasingly combined with multimodal imaging, including electrocardiography (ECG), echocardiography, chest radiography, cardiac computed tomography (CT), and cardiac magnetic resonance imaging (MRI). These modalities, when analyzed through deep learning frameworks, enable more precise characterization of cardiac structure and function, enhancing diagnostic accuracy and facilitating personalized clinical decision-making.
The review also highlights emerging AI-driven biomarker discovery methods that extend beyond traditional clinical data. Novel approaches, such as face recognition, fundus photography, speech recognition, and phonocardiogram analysis, allow subtle physiological changes to be detected noninvasively. These technologies offer promising avenues for early identification of HF-related abnormalities.
In the domain of disease monitoring, AI is being integrated with wearable and implantable devices to enable real-time tracking of key physiological parameters, including heart rate, rhythm, blood pressure, and cardiac function. These systems support continuous assessment of disease status, thereby enabling timely intervention and reducing the risk of HF exacerbation.
AI also plays an increasingly important role in therapeutic decision-making. It facilitates the identification of novel biomarkers and molecular pathways, providing new insights into HF pathophysiology. In interventional cardiology, AI assists in patient selection and outcome prediction for procedures such as transcatheter aortic valve implantation (TAVI), cardiac resynchronization therapy (CRT), and left ventricular assist device implantation (LVAD). In addition, AI-driven clinical decision-support systems (AI-CDSS) help standardize treatment strategies, reduce variability in clinical practice, and optimize individualized care pathways. The development of virtual HF wards further extends care beyond hospital settings, enabling remote monitoring and timely management.
Importantly, the authors emphasize that AI is not merely a tool for isolated clinical tasks but is reshaping the entire HF care ecosystem. By integrating multimodal data sources, advanced algorithms, and clinical workflows, AI enables a closed-loop management model that spans screening, diagnosis, monitoring, treatment, and long-term follow-up.
Nevertheless, several challenges remain before AI can be fully translated into routine clinical practice. The challenges include limited model generalizability due to data heterogeneity and selection bias, insufficient interpretability that constrains clinical trust, and reliability concerns. Addressing these issues requires multicenter real-world validation, development of interpretable hybrid models, and deployment of AI systems with robust safeguards.
Overall, this review not only synthesizes current advances in AI-enabled HF management but also provides practical insights into their clinical applicability and real-world limitations, offering valuable guidance for future research and translation into practice.
“ Through continuously optimizing algorithms, improving data quality, and addressing ethical issues, AI is expected to provide more efficient and accurate management plans for HF patients, ultimately improving quality of life ,” concludes Prof. Tang.
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Reference
DOI: 10.1097/CM9.0000000000004000
About Conghui Zhang from Beihang University
Conghui Zhang, Ph.D. candidate at Beihang University, focuses on algorithm development and clinical translational research of artificial intelligence in cardiovascular medicine.
About Zhiyun Yang from Peking University Third Hospital
Zhiyun Yang, M.D., Ph.D., is an Attending Cardiologist at Peking University Third Hospital and a member of the Heart Failure Association of the European Society of Cardiology (ESC-HFA). His research focuses on the clinical application of artificial intelligence in cardiovascular diseases, with more than ten high-quality publications in this field.
About Yi-Da Tang from Peking University Third Hospital
Yi-Da Tang, M.D., Ph.D., is a Professor and Chief Physician at Peking University Third Hospital. He has long been dedicated to the prediction and early warning of acute myocardial infarction and end-stage heart diseases (such as heart failure), as well as the development of novel diagnostic and therapeutic strategies. His research also focuses on the mechanisms underlying metabolic cardiovascular diseases. His recent work has been published in leading journals, including Circulation , JAMA Cardiology , and Cell Reports Medicine .
Funding information
The study was granted by the National Natural Science Foundation of China (No. T2541009), and Peking University Medicine plus X Pilot Program–Artificial Intelligence and Medical Development Initiative (No. BMU2025YXXLHAIYX014).
Chinese Medical Journal
Literature review
Not applicable
Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis
5-Mar-2026