Evidence map›Paper›PMID 41510734›Full record

ReviewCurrent cardiology reviews2026

Exploring the Potential of AI and Augmented Reality in Cardiovascular Disease Management: A Narrative Review.

Aadil Mahmood Khan, Arlette Villalobos, Akhil Dhanjibhai Kakadiya, Harleen Kaur, Sara Tabassum, Ansari Maha Faisal, Rutvika Pardeshi, Dhavan Shah, Sarath Chandra Ponnada, Krish Patel

Abstract readReview
In one paragraph

Review in Current cardiology reviews, 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

10 authors.

Aadil Mahmood KhanOSF Saint Francis Medical Centre, Peoria, Illinois, IL 61637, USA.
Arlette VillalobosPonce Health Sciences University, Ponce, 00716, USA.ORCID 0000-0001-8861-4055
Akhil Dhanjibhai KakadiyaGMERS Medical College & Hospital, Sola, Ahmedabad, India.
Harleen KaurGovernment Medical College, Patiala, India.
Sara TabassumDr. V.R.K Women's Medical College, Telangana, Aziz Nagar, India.
Ansari Maha FaisalDanylo Halytsky Lviv National Medical University, Ukraine, Lviv, Ukraine.
Rutvika PardeshiGCS Medical College, Hospital and Research Center, Ahmedabad, India.
Dhavan ShahB.J. Medical College, Ahmedabad, India.
Sarath Chandra PonnadaGreat Eastern Medical School and Hospital, Srikakulam, India.ORCID 0009-0008-2324-7846
Krish PatelGovernment Medical College, Surat, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCardiovascular diseases remain a leading cause of morbidity and mortality worldwide, with their rising incidence demanding a shift toward more personalized treatment approaches. Artificial intelligence (AI) and augmented reality (AR) are two newly evolving technologies that have found extensive usage in the field of cardiovascular medicine and surgery. AI-based models involve machine learning and deep learning neural networks. These primarily form the basis of prediction models, allowing the prediction of risk, survival, and risk stratification of patients.

methodsA literature search was conducted using PubMed and Google Scholar, and it included studies published between 2003 and 2024. Articles were selected based on clinical relevance and applicability to cardiovascular disease management using artificial intelligence (AI) and AR. Keywords used included "cardiovascular disease", "artificial intelligence", "augmented reality", "diagnostic imaging", and "risk prediction". Studies were screened manually for inclusion based on the title and abstract review, followed by full-text evaluation for relevance and quality.

resultsThis narrative review highlights how artificial intelligence (AI) and augmented reality (AR) are increasingly being applied in cardiovascular disease management. Despite recent studies, there remains a lack of proper evaluation of these models' efficacy, and therefore multiple large-scale trials are needed. DISCUSSION: Networks such as Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP) have been used to improve image interpretation and documentation processes.

conclusionFurther and larger studies are needed to test the efficacy and safety of these models. This narrative review summarizes recent findings in AI and AR and offers perspectives on future research.

Indexed as

Artificial IntelligenceAugmented RealityCardiovascular DiseasesDisease ManagementDigital HealthHumansArtificial intelligenceaugmented realitycardiovascular diseasecardiovascular disease managementprediction modelsrisk stratification

Identifiers

PMID41510734
PMCPMC13519555

What OpenQuestion holds

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Registered trials

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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.