Evidence map›Paper›PMID 38357567›Full record

ReviewJournal of cardiovascular and thoracic research2023

Artificial intelligence in cardiovascular medicine: An updated review of the literature.

Arian Zargarzadeh, Elnaz Javanshir, Alireza Ghaffari, Erfan Mosharkesh, Babak Anari

Open access · diamondAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of cardiovascular and thoracic research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
6.6field-weighted citation impact, top 2% of its field
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

16 citing papers in PubMed, 30 citations in OpenAlex.

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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

5 authors at 3 institutions in 1 country.

Arian ZargarzadehCentral Toronto Academy, Toronto, Canada.ORCID https://orcid.org/0009-0009-3596-724X
Elnaz JavanshirCardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Alireza GhaffariFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.ORCID https://orcid.org/0009-0001-8311-1253
Erfan MosharkeshFaculty of Veterinary Medicine, University of Tabriz, Tabriz, Iran.ORCID https://orcid.org/0000-0003-2835-2449
Babak AnariDepartment of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran.
University of Tabriz · IRIslamic Azad University Shabestar · IRTabriz University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Screening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.

Indexed as

Artificial intelligenceCardiovascular diseaseHealthcare

Identifiers

PMID38357567
PMCPMC10862032
OpenAlexW4390844177

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.