The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.

Philip M Croon, Lovedeep S Dhingra, Aline F Pedroso, Rohan Khera

Abstract readReview

In one paragraph

Review in Current heart failure reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Philip M CroonSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 100 Church St. South, Suite F250, New Haven, CT, 06519, USA.

Lovedeep S DhingraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 100 Church St. South, Suite F250, New Haven, CT, 06519, USA.

Aline F PedrosoSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 100 Church St. South, Suite F250, New Haven, CT, 06519, USA.

Rohan KheraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 100 Church St. South, Suite F250, New Haven, CT, 06519, USA. rohan.khera@yale.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewArtificial intelligence (AI) is poised to transform heart failure (HF) care across the clinical continuum, yet a substantial gap remains between model development and implementation. This review aims to summarize key AI-enabled innovations across HF care and provide a practical framework for clinical implementation. RECENT

findingsAI applications based on electrocardiography, echocardiography, electronic health records, wearable devices, and large language models have demonstrated promise for early detection, diagnosis, risk stratification, and treatment optimization in HF. The field is shifting from retrospective model development toward prospective evaluation, workflow integration, and health-system deployment, though challenges related to bias, generalizability, interoperability, and clinician adoption persist. Here, we propose a practical stepwise framework to support the safe, scalable, and sustainable implementation of AI in real-world heart failure care.

Indexed as

Artificial IntelligenceCardiomyopathiesHeart FailureDigital HealthElectrocardiographyHumansArtificial intelligenceClinical implementationDigital healthElectrocardiographyHeart failure

Identifiers

PMID42165933

DOI10.1186/s12911-023-02115-5

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.