Evidence map›Paper›PMID 41712043›Full record

ReviewHeart failure reviews2026

Artificial intelligence and digital health in heart failure: advances in diagnosis, monitoring, phenotyping, and digital biomarkers.

George Perlman, Janie Leroux, Carlos Octavio Pérez Mendoza, Abhinav Sharma

Abstract readReview
PubMed Publisher
In one paragraph

Review in Heart failure reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

George PerlmanResearch Institute of the McGill University Health Centre, Montreal, Canada.
Janie LerouxResearch Institute of the McGill University Health Centre, Montreal, Canada.
Carlos Octavio Pérez MendozaResearch Institute of the McGill University Health Centre, Montreal, Canada.
Abhinav SharmaResearch Institute of the McGill University Health Centre, Montreal, Canada. Abhinav.sharma@mcgill.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart failure (HF) is an increasingly pressing public health concern, with projections suggesting its age-standardized prevalence will increase in coming decades. To accommodate the increasing burden that will be placed on patients and healthcare systems, applications of digital health technologies including artificial intelligence (AI) may improve aspects of HF care and monitoring. However, adoption of digital health solutions into HF clinical practice has been modest to date due to limited evidence of efficacy as well as technical, clinical implementation, and regulatory barriers. In this review, we discuss the state of the art in digital health technologies for HF, including applications of AI for medical image acquisition and processing, monitoring tools including remote patient management platforms and wearable devices, and phenotyping patients using actigraphy-enabled wearables. We further highlight potential solutions for barriers to the uptake of novel technologies and development of digital biomarkers in HF care and research. Together, this review underscores the potential for digital health and AI tools to improve HF outcomes and prepares stakeholders to implement these technologies effectively.

Indexed as

Artificial IntelligenceHeart FailureBiomarkersDigital HealthHumansPhenotypeRemote Patient MonitoringTelemedicineWearable Electronic DevicesBiomarkersArtificial intelligenceDigital healthHeart failureRemote patient managementWearables

Identifiers

PMID41712043

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.