Advances and prospects of artificial intelligence in precision phenotyping of heart failure

Corresponding author: Zhai Ning, 519236005@qq.com

DOI: 10.12201/bmr.202512.00030

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  Abstract: Heart failure (HF) is a highly heterogeneous clinical syndrome, and the traditional classification based on left ventricular ejection fraction (LVEF) is increasingly unable to meet the demands of precision medicine. In recent years, the rapid development of artificial intelligence (AI) technology has opened new avenues for the precision phenotyping of HF. By leveraging unsupervised learning to uncover hidden data patterns or supervised learning to validate phenotypes and predict risks, AI technologies can automatically analyze multi-modal data, including electronic health records (EHR), cardiac imaging, and multi-omics. These approaches assist clinicians in identifying novel disease subtypes that are difficult to distinguish using traditional methods, optimizing risk stratification for high-risk patients, matching personalized treatments to specific phenotypes, and addressing clinical trust issues by enhancing model interpretability, while also revealing new pathophysiological mechanisms. Although AI phenotyping currently faces challenges such as data limitations, the black box nature of algorithms, and difficulties in clinical translation, it is driving the transformation of HF management toward a precision medicine model driven by biological mechanisms.

  Key words: Heart Failure; Artificial Intelligence; Machine Learning; Phenotyping; Heterogeneity; Precision Medicine; Risk Stratification

  Submit time: 10 December 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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