SynthesisVascular health and risk management2026
Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges.
Synthesis in Vascular health and risk management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide. Conventional risk prediction models often demonstrate suboptimal calibration and limited generalizability across populations. Artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), enable integration of multimodal clinical and imaging data for individualized cardiovascular risk estimation. Objective: To evaluate the applications, predictive performance, and translational limitations of AI models for cardiovascular risk prediction within an umbrella review framework. Methods: PubMed, Scopus, and Web of Science were systematically searched for studies published between January 2015 and October 2025 investigating AI-based prediction of cardiovascular outcomes. Eligible designs included randomized controlled trials (RCTs), cohort studies, systematic reviews, and meta-analyses. Predictive performance was the primary outcome, mainly assessed using the area under the receiver operating characteristic curve (AUC). Methodological quality was evaluated using established risk-of-bias tools. From 3500 identified records, 48 studies (8 RCTs, 28 cohort studies, and 12 systematic reviews or meta-analyses) were included in the final analysis. Results: AI models achieved AUC values greater than 0.90 in more than 70% of imaging-based studies. Evidence synthesis showed predominant reliance on internal validation, inconsistent calibration reporting, and limited evaluation of algorithmic fairness. Multimodal data integration improved detection of coronary artery disease (CAD) and heart failure (HF). Wearable monitoring was associated with 18-25% lower hospitalization rates compared with usual care. Conclusion: AI improves predictive accuracy in cardiovascular risk assessment. Despite strong discrimination performance (AUC), methodological heterogeneity, insufficient calibration assessment, algorithmic bias, limited external validation, and regulatory uncertainty remain major barriers to implementation. Clinical translation requires multicenter RCTs, explainable AI frameworks, and standardized reporting guidelines such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Artificial Intelligence (TRIPOD-AI).
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What Socratic 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 Socratic graph.