# Artificial intelligence in heart failure

## TL;DR

This paper reviews how artificial intelligence is being used to improve heart failure care, from diagnosis to treatment and telecare, and highlights future directions.

## Contribution

The paper provides a comprehensive overview of AI applications across all stages of heart failure management and identifies current limitations and future research needs.

## Key findings

- AI is being applied in heart failure diagnosis, subtyping, and prognosis.
- Current AI applications face challenges in clinical translation and validation.
- Future research aims to develop mature AI systems for heart failure treatment.

## Abstract

Heart failure (HF) affects millions of individuals worldwide and shows an increasing trend, constituting a serious public health issue. Considerable attention has been paid to the screening, diagnosis, risk prediction, treatment, and prognosis of HF. Although many guidelines for the management of HF have been proposed in recent years, the efficacy of evidence-based treatments seems to vary among patients. Therefore, the era of “one-size-fits-all” approaches is drawing to a close, and the concepts of precision medicine and individualized medicine are gradually taking root. Artificial intelligence (AI) is an emerging discipline in the rapidly growing field of computer science. It has now become deeply involved in all aspects of cardiovascular disease research, with particular relevance to HF, though its translation into clinical practice is yet to be fully realized. Although the use of AI in cardiovascular disease (CVD) and HF patient care, as well as cardiac resynchronization therapy (CRT), has been extensively discussed, a discussion from the standpoint of all aspects of HF clinical process is lacking.

This review provides a comprehensive overview of the use of AI in HF in specific scenarios, including patient diagnosis, subtyping, prognostic assessment, pre- and post-treatment evaluation, and telecare. It also presents the prospects and challenges for the development of AI in the field of HF, with the expectation that a mature AI diagnosis and treatment system adapted to clinical practice will be developed in the future through in-depth research and validation.

This review summarizes the application of AI in various links of HF management from diagnosis to telecare, and analyzes its current application limitations, existing challenges and future research directions, aiming to provide a reference for the subsequent clinical transformation and research optimization of AI in the HF field.

The online version contains supplementary material available at 10.1186/s43044-026-00723-w.