Evidence map›Paper›PMID 39871619›Full record

ReviewClinical cardiology2025

Advancements in Artificial Intelligence in Noninvasive Cardiac Imaging: A Comprehensive Review.

Oluwaremilekun Zeth Tolu-Akinnawo, Francis Ezekwueme, Olukunle Omolayo, Sasha Batheja, Toluwalase Awoyemi

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  11. Artificial Intelligence in Cardiovascular Health: Insights into Post-COVID Public Health Challenges.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2025
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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.

Oluwaremilekun Zeth Tolu-AkinnawoDepartment of Internal Medicine, Meharry Medical College, Nashville, Tennessee, USA.ORCID http://orcid.org/0009-0007-2871-6878
Francis EzekwuemeDepartment of Internal Medicine, University of Pittsburgh Medical Center, McKeesport, Pennsylvania, USA.
Olukunle OmolayoDepartment of Internal Medicine, Lugansk State Medical University, Lugansk, Ukraine.
Sasha BathejaDepartment of Internal Medicine, Government Medical College, Patiala, Punjab, India.
Toluwalase AwoyemiDepartment of Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.ORCID http://orcid.org/0000-0003-1542-9435

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTechnological advancements in artificial intelligence (AI) are redefining cardiac imaging by providing advanced tools for analyzing complex health data. AI is increasingly applied across various imaging modalities, including echocardiography, magnetic resonance imaging (MRI), computed tomography (CT), and nuclear imaging, to enhance diagnostic workflows and improve patient outcomes. HYPOTHESIS: Integrating AI into cardiac imaging enhances image quality, accelerates processing times, and improves diagnostic accuracy, enabling timely and personalized interventions that lead to better health outcomes.

methodsA comprehensive literature review was conducted to examine the impact of machine learning and deep learning algorithms on diagnostic accuracy, the detection of subtle patterns and anomalies, and key challenges such as data quality, patient safety, and regulatory barriers.

resultsFindings indicate that AI integration in cardiac imaging enhances image quality, reduces processing times, and improves diagnostic precision, contributing to better clinical decision-making. Emerging machine learning techniques demonstrate the ability to identify subtle cardiac abnormalities that traditional methods may overlook. However, significant challenges persist, including data standardization, regulatory compliance, and patient safety concerns.

conclusionsAI holds transformative potential in cardiac imaging, significantly advancing diagnosis and patient outcomes. Overcoming barriers to implementation will require ongoing collaboration among clinicians, researchers, and regulatory bodies. Further research is essential to ensure the safe, ethical, and effective integration of AI in cardiology, supporting its broader application to improve cardiovascular health.

Indexed as

Artificial IntelligenceCardiac Imaging TechniquesHeart DiseasesHumansMachine LearningAI (artificial intelligence)cardiac imaging techniqueshealth care reformsprecision medicine

Identifiers

PMID39871619
PMCPMC11772728

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.