ReviewEuropean cardiology2022
Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold.
Review in European cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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.
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.
Who cites it
28 citing papers in PubMed.
- Implementation of cardiovascular disease risk prediction scores in Sub-Saharan Africa: a scoping review.BMC health services research · 2026Article
- A scoping review on artificial intelligence-based tools for cardiovascular disease risk prediction.BMC medical informatics and decision making · 2026Article
- An Artificial Intelligence based model for predicting long-term all-cause mortality after acute Myocardial Infarction (the AIMI model).European heart journal. Digital health · 2026Article
- Artificial Intelligence in Cardiovascular Disease Prevention: Current Applications and Future Perspectives.Anatolian journal of cardiology · 2026Review
- Utility of Field Tests for Predicting Cardiorespiratory Fitness and Prescribing Exercise Intensity in Cardiac Rehabilitation Programs: A Randomized Crossover Trial.Journal of cardiovascular development and disease · 2026Article
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Applications of artificial intelligence in cardiovascular risk detection and prediction among adults with obesity: a scoping review.Frontiers in cardiovascular medicine · 2026Review
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular medicine · 2026Article
- Machine learning-based risk prediction of outcomes in patients hospitalized with COVID-19 in Australia: the AUS-COVID Score.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Three-Dimensional Perspectives on Inflammatory Regulation in Coronary Atherosclerosis: Integrated Mechanisms of Endothelial Priming, Lipid Metabolism, and Cytokine Synergy.Reviews in cardiovascular medicine · 2026Review
- Comparison of Supervised Machine Learning Models to Logistic Regression Model Using Tooth-Related Factors to Predict the Outcome of Nonsurgical Periodontal Treatment.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Advancing cardiovascular risk assessment.Cardiovascular research · 2025Article
- Review
- Predicting survival in patients with severe heart failure: Risk score validation in the HELP-HF cohort.European journal of heart failure · 2025Observational
- AI-assisted heart failure management: A review of clinical applications, case studies, and future directions.Global cardiology science & practice · 2025Review
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- AI-derived automated quantification of cardiac chambers and myocardium from non-contrast CT: Prediction of major adverse cardiovascular events in asymptomatic subjects.Atherosclerosis · 2025Article
- Article
- Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.European heart journal. Digital health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is a broad term referring to any automated systems that need 'intelligence' to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the dissemination of cardiovascular risk factors and the better prognosis of patients experiencing cardiovascular events resulted in an increase in the prevalence of cardiovascular disease (CVD), eliciting the need for precise identification of patients at increased risk for development and progression of CVD. AI-based predictive models may overcome some of the limitations that hinder the performance of classic regression models. Nonetheless, the successful application of AI in this field requires knowledge of the potential pitfalls of the AI techniques, to guarantee their safe and effective use in daily clinical practice. The aim of the present review is to summarise the pros and cons of different AI methods and their potential application in the cardiovascular field, with a focus on the development of predictive models and risk assessment tools.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.