Evidence map›Paper›PMID 35855754›Full record

ReviewVascular health and risk management2022

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects.

Ikram U Haq, Karanjot Chhatwal, Krishna Sanaka, Bo Xu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Vascular health and risk management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  16. Role of artificial intelligence in cardiology.World journal of cardiology · 2023
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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

4 authors.

Ikram U HaqDepartment of Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
Karanjot ChhatwalImperial College London School of Medicine, London, SW7 2AZ, UK.
Krishna SanakaEmory University, Atlanta, GA, 30322, USA.ORCID 0000-0003-2583-8726
Bo XuSection of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.ORCID 0000-0002-2985-7468

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.

Indexed as

Coronary Artery DiseaseHeart FailureHeart Valve DiseasesAlgorithmsArtificial IntelligenceHumansMulticenter Studies as TopicProspective Studiesartificial intelligencecardiovascular medicinemachine learningneural networks

Identifiers

PMID35855754
PMCPMC9288176

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.