Evidence mapPaperPMID 40956375Full record

ReviewHigh blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension2025

Artificial Intelligence in Cardiovascular Health: Insights into Post-COVID Public Health Challenges.

Zayera Naushad, Jaya Malik, Abhishek Kumar Mishra, Shilpy Singh, Dharmsheel Shrivastav, Chetan Kumar Sharma, Ved Vrat Verma, Ravi Kant Pal, Biswajit Roy, Varun Kumar Sharma

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In one paragraph

Review in High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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.

Zayera Naushad *Department of Biotechnology and Microbiology, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
Jaya Malik *Department of Biotechnology and Microbiology, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
Abhishek Kumar MishraDepartment of Biotechnology and Microbiology, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
Shilpy SinghDepartment of Biotechnology and Microbiology, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
Dharmsheel ShrivastavDepartment of Biotechnology and Microbiology, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
Chetan Kumar SharmaDepartment of Mathematics, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
Ved Vrat VermaDepartment of Biotechnology, School of Engineering and Technology, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India.
Ravi Kant PalNational Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, 110067, India.
Biswajit RoyInformation Technology Group CSIR-Centre for Cellular and Molecular Biology, Habsiguda, Uppal Road, Hyderabad, 500 007, Telangana, India. broy@ccmb.res.in.
Varun Kumar SharmaDepartment of Biotechnology and Microbiology, School of Sciences, Noida International University, GautamBudh Nagar, Uttar Pradesh, 201308, India. varungenetics@gmail.com.ORCID http://orcid.org/0000-0001-8575-6939

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular diseases (CVDs) continue to be the topmost cause of the worldwide morbidity and mortality. Risk factors such as diabetes, hypertension, obesity and smoking are significantly worsening the situation. The COVID-19 pandemic has powerfully highlighted the undeniable connection between viral infections and cardiovascular health. Current literature highlights that SARS-CoV-2 contributes to myocardial injury, endothelial dysfunction, thrombosis, and systemic inflammation, increasing the severity of CVD outcomes. Long COVID has also been associated with persistent cardiovascular complications, including myocarditis, arrhythmias, thromboembolic events, and accelerated atherosclerosis. Addressing these challenges requires continued research and public health strategies to mitigate long-term risks. Artificial intelligence (AI) is changing cardiovascular medicine and community health through progressive machine learning (ML) and deep learning (DL) applications. AI enhances risk prediction, facilitates biomarker discovery, and improves imaging techniques such as echocardiography, CT, and MRI for detecting coronary artery disease and myocardial injury on time. Remote monitoring and wearable devices powered by AI enable real-time cardiovascular assessment and personalized treatment. In public health, AI optimizes disease surveillance, epidemiological modeling, and healthcare resource allocation. AI-driven clinical decision support systems improve diagnostic accuracy and health equity by enabling targeted interventions. The integration of AI into cardiovascular medicine and public health offers data-driven, efficient, and patient-centered solutions to mitigate post-COVID cardiovascular complications.

Indexed as

Artificial IntelligenceCardiovascular DiseasesCOVID-19Public HealthHumansRisk AssessmentRisk FactorsSARS-CoV-2Artificial intelligenceCardiovascular diseasesCOVID-19Post-COVID-19Public health

Identifiers

PMID40956375

What Socratic holds

Textmetadata
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 Socratic graph.