Evidence map›Paper›PMID 37351333›Full record

ArticleCardiovascular digital health journal2023

Artificial intelligence-enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges.

Alexander Schepart, Arianna Burton, Larry Durkin, Allison Fuller, Ellyn Charap, Rahul Bhambri, Faraz S Ahmad

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cardiovascular digital health journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  5. Clinical cognition in the age of cardiovascular AI.Frontiers in cardiovascular medicine · 2026
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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

7 authors.

Alexander SchepartPfizer Inc, New York, New York.
Arianna BurtonPfizer Inc, New York, New York.
Larry DurkinVPMR LLC, Kennett Square, Pennsylvania.
Allison FullerVPMR LLC, Kennett Square, Pennsylvania.
Ellyn CharapVPMR LLC, Kennett Square, Pennsylvania.
Rahul BhambriPfizer Inc, New York, New York.
Faraz S AhmadNorthwestern University Feinberg School of Medicine, Chicago, Illinois.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Numerous artificial intelligence (AI)-enabled tools for cardiovascular diseases have been published, with a high impact on public health. However, few have been adopted into, or have meaningfully affected, routine clinical care. Objective: To evaluate current awareness, perceptions, and clinical use of AI-enabled digital health tools for patients with cardiovascular disease, and challenges to adoption. Methods: This mixed-methods study included interviews with 12 cardiologists and 8 health information technology (IT) administrators, and a follow-on survey of 90 cardiologists and 30 IT administrators. Results: We identified 5 major challenges: (1) limited knowledge, (2) insufficient usability, (3) cost constraints, (4) poor electronic health record interoperability, and (5) lack of trust. A minority of cardiologists were using AI tools; more were prepared to implement AI tools, but their sophistication level varied greatly. Conclusion: Most respondents believe in the potential of AI-enabled tools to improve care quality and efficiency, but they identified several fundamental barriers to wide-scale adoption.

Indexed as

Artificial intelligenceCardiologyCardiovascular diseaseDigital health toolElectronic health recordsMachine learningSurvey

Identifiers

PMID37351333
PMCPMC10282011

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.