Evidence map›Paper›PMID 42846957›Full record

ReviewEuropean heart journal. Digital health2026

Artificial intelligence in cardiovascular medicine: a practical guide for clinicians and researchers.

Christian Basile, Alessandro Villaschi, Pasquale Ambrosino, Mario Enrico Canonico, Araz Rawshani, Emanuele Bobbio

Abstract readReview
PubMed Full text at PMC Publisher
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

6 authors.

Christian BasileDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.ORCID https://orcid.org/0000-0002-4680-3058
Alessandro VillaschiDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
Pasquale AmbrosinoIstituti Clinici Scientifici Maugeri IRCCS, Scientific Directorate, Telese Terme Institute, Telese Terme, Italy.ORCID https://orcid.org/0000-0002-9398-0428
Mario Enrico CanonicoCPC Clinical Research, Aurora, CO, USA.
Araz RawshaniDepartments of Cardiology, Sahlgrenska University Hospital, Blå stråket 3, SE-413 46 Gothenburg, Sweden.
Emanuele BobbioDepartments of Cardiology, Sahlgrenska University Hospital, Blå stråket 3, SE-413 46 Gothenburg, Sweden.ORCID https://orcid.org/0000-0002-8287-2448

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming cardiovascular medicine by extracting clinically relevant patterns from complex digital health data, extending beyond traditional statistical approaches. However, the translation of AI from proof-of-concept studies to routine cardiovascular care remains constrained by methodological, ethical, and regulatory challenges. Key barriers include data quality and representativeness, model generalizability, calibration and clinical utility, interpretability, and the risk of bias amplification. To address this gap, this review synthesizes current evidence on AI applications in cardiology, demystifies foundational machine learning concepts for a clinical audience, and outlines a practical framework for best practices in model development, evaluation, and responsible clinical deployment.

Indexed as

Artificial intelligenceCardiac imagingCardiovascular medicineMachine learningOutcomePrecision medicine

Identifiers

PMID42846957
PMCPMC13644533

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.