Evidence map›Paper›PMID 42846468›Full record

ReviewEuropean heart journal. Digital health2026

Artificial intelligence to support clinical practice across the inpatient heart failure care pathway: a systematic review.

Elias V Wolf, Miriam Grapp, Mechthild Hartmann, Hans-Christoph Friederich, Till Johannes Bugaj

Abstract readReview
In one paragraph

Review in European heart journal. Digital health, 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

5 authors.

Elias V WolfDepartment of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Im Neuenheimer Feld 460, Heidelberg 69120, Germany.ORCID https://orcid.org/0000-0001-9342-5224
Miriam GrappDepartment of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Im Neuenheimer Feld 460, Heidelberg 69120, Germany.ORCID https://orcid.org/0000-0003-3837-0931
Mechthild HartmannDepartment of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Im Neuenheimer Feld 460, Heidelberg 69120, Germany.ORCID https://orcid.org/0000-0002-9329-1104
Hans-Christoph FriederichDepartment of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Im Neuenheimer Feld 460, Heidelberg 69120, Germany.ORCID https://orcid.org/0000-0003-4344-8959
Till Johannes BugajDepartment of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Im Neuenheimer Feld 460, Heidelberg 69120, Germany.ORCID https://orcid.org/0000-0001-8287-2635

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart failure is a complex clinical syndrome requiring repeated reassessment, close monitoring, and timely treatment decisions throughout hospitalization. With the increasing emergence of artificial intelligence applications, there is growing interest in how artificial intelligence-based tools may support physicians along the inpatient heart failure care pathway. This systematic review assessed artificial intelligence applications supporting adult patients with heart failure from admission through inpatient management to discharge. The review followed PRISMA guidelines and was prospectively registered in PROSPERO (CRD420261336670). PubMed, Web of Science, and Embase were searched through February 2026. Eligible studies evaluated artificial intelligence-based interventions or tools relevant to inpatient heart failure care. Studies limited to intensive care, isolated imaging settings, classic telemedicine, or theoretical prediction models without demonstrated clinical use were excluded. Study selection was performed using Covidence, and methodological quality was assessed with STROBE criteria for observational studies and RoB 2 for randomized controlled trials. 19 studies were included, comprising 16 observational studies and three randomized controlled trials. Seven studies addressed admission, four inpatient management, and eight discharge-related applications. During admission, artificial intelligence mainly supported early recognition of heart failure and reduced ejection fraction, with the strongest evidence for ECG-based models. In inpatient care, applications were heterogeneous and supported clinical decision-making, patient identification, and medication adherence prediction, using electronic health record-derived data. At discharge, large language models and digital tools mainly supported patient education and self-management, although limited actionability and implementation barriers remained. Artificial intelligence may support inpatient heart failure care, but evidence of measurable clinical benefit remains limited.

Indexed as

AdmissionArtificial intelligenceDischargeHeart failureInpatient care

Identifiers

PMID42846468
PMCPMC13644536

What OpenQuestion holds

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Registered trials

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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.