Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges

Introduction

Cardiovascular diseases (CVDs) remain the leading cause of mortality globally. Updated estimates from the Global Burden of Disease (GBD) collaboration reported approximately 20.5 million CVD-related deaths in 2023, with the majority occurring in low- and middle-income countries (LMICs) where diagnostic and therapeutic resources remain limited.^1,2 In the United States (US), the American Heart Association (AHA) reports one CVD-related death every 33 seconds, corresponding to nearly 700,000 deaths annually and generating an economic burden exceeding USD 400 billion in direct and indirect healthcare costs.³ According to the European Society of Cardiology (ESC), CVD accounts for 47% of female and 39% of male deaths across Europe, with pronounced regional disparities favoring Western over Eastern regions due to differences in risk factor management and healthcare infrastructure.⁴ In Asia, projections indicate a 91.2% increase in CVD mortality between 2025 and 2050, largely driven by population aging and escalating metabolic risk factors.⁵ Moreover, a recent global report confirmed that cardiovascular deaths attributable to metabolic risk factors increased by more than 18% between 2010 and 2021, underscoring the urgent need for predictive and preventive strategies supported by advanced data-driven technologies.⁶

Conventional risk stratification tools, such as the Framingham Risk Score (FRS), estimate the 10-year probability of coronary events using a limited set of clinical variables, including age, systolic blood pressure (SBP), total cholesterol, and smoking status.⁷ In Europe, the Systematic COronary Risk Evaluation 2 (SCORE2) and its older-adult extension (SCORE2-OP) provide risk estimation for fatal and non-fatal cardiovascular events with improved calibration across European risk regions.⁸ However, LMICs account for over 80% of CVD-related deaths, primarily due to restricted access to diagnostic and therapeutic tools. Systematic evaluations indicate that conventional scores, including FRS and SCORE2, frequently exhibit miscalibration in non-Caucasian populations, overestimating risk in low-incidence groups and underestimating it in high-risk cohorts. These models also fail to incorporate emerging contributors such as physical inactivity, psychosocial stress, and novel biomarkers, limiting their generalizability and predictive precision.⁹ More recently, contemporary risk scores such as the Predicting Bleeding Complications in Patients Undergoing Stent Implantation–High Bleeding Risk (PRECISE-HBR) score, a well-validated seven-item bleeding risk prediction tool for patients undergoing percutaneous coronary intervention, have demonstrated improved discrimination compared with earlier bleeding risk models and are increasingly incorporated into data-driven algorithmic decision-support systems.¹⁰ Artificial intelligence (AI) approaches not only augment preventive strategies but also hold promise in acute cardiovascular care, overcoming limitations of conventional methods and providing complementary or integrative pathways to advance precision in cardiovascular risk assessment.^11–13

AI techniques, including machine learning (ML) and deep learning (DL) algorithms, possess superior capacity to analyze high-dimensional, multimodal datasets derived from electronic health records (EHRs), cardiac imaging, genomic profiling, and wearable sensor data. By identifying nonlinear and latent relationships among diverse risk determinants, AI models have demonstrated predictive performance that exceeds conventional statistical frameworks.^11,14 Meta-analytic evidence further indicates that AI-enhanced prediction systems achieve significantly higher discrimination for myocardial infarction (MI) and heart failure (HF) onset compared with guideline-endorsed risk scores.¹²

Despite these promising results, the clinical translation of AI-driven CVD prediction remains limited. Key challenges include heterogeneity of training datasets, algorithmic opacity, bias amplification in underrepresented populations, and uncertainties regarding regulatory frameworks governing data privacy, transparency, and model accountability.¹⁵ Current clinical guidelines, including the 2021 ESC Guidelines on cardiovascular disease prevention, acknowledge the emerging role of AI in risk prediction but emphasize the necessity of rigorous external validation, transparency, and regulatory oversight prior to routine clinical implementation.⁷ Accordingly, this umbrella review assesses the practical applications and limitations of AI in cardiovascular risk prediction, with a focus on clinical implementation barriers and translational challenges, such as under-evaluation of model calibration, external validity, and algorithmic fairness, rather than solely technical or theoretical performance. The review also proposes targeted strategies to overcome these barriers and to promote interdisciplinary collaboration for equitable, safe, and effective integration of AI into routine precision cardiology practice.

Methods

Search Strategy and Data Sources

A comprehensive systematic search was conducted to identify evidence on the application of AI in CVD prediction. Three major electronic databases (PubMed, Scopus, and Web of Science) were searched for studies published between January 2015 and October 2025 to capture recent advances in the field.

The search strategy combined Medical Subject Headings (MeSH) with free-text keywords, including artificial intelligence, machine learning, deep learning, cardiovascular risk prediction, predictive modeling, cardiac imaging analysis, wearable sensors, and precision cardiovascular care. Boolean operators (AND, OR, NOT) were applied to refine the search and improve retrieval accuracy.

Reference lists of included studies, relevant review articles, and American College of Cardiology (ACC) guideline documents were manually screened to identify additional sources. Non-peer-reviewed materials, including conference proceedings, preprints, and trial registry records, were excluded.

Eligibility Criteria

Studies were selected according to predefined inclusion and exclusion criteria aligned with the objectives of this systematic review.

Inclusion Criteria

Eligible studies were required to:

• Be published between January 2015 and October 2025.
• Apply AI techniques such as ML, DL, or natural language processing (NLP) to predict CVD risk, events, or outcomes.
• Report quantitative performance metrics, including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, or comparisons with conventional risk scores.
• Use robust study designs such as randomized controlled trials (RCTs), prospective cohort studies, or meta-analyses with verifiable primary data.
• For secondary evidence (systematic reviews and meta-analyses), include high-quality syntheses reporting pooled performance metrics; these were used only for contextual interpretation and not for quantitative pooling.

Exclusion Criteria

Studies were excluded if they:

• Were published in languages other than English.
• Examined AI applications unrelated to CVDs.
• Were editorials, letters, or abstracts without original data.
• Lacked accessible full text.

Study Selection Process

Two independent reviewers screened titles and abstracts to minimize selection bias. Disagreements were resolved through discussion or adjudication by a third reviewer. The selection process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure transparency and reproducibility.¹⁶ The review protocol was not prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO); however, the review followed a predefined methodological framework to maintain transparency and reproducibility (Figure 1).

Figure 1 Systematic flow diagram of the study selection process. **If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools. *Records Identified: A total of 3500 records were retrieved from electronic databases, including PubMed (n = 1200), Scopus (n = 1400), and Web of Science (n = 900). An additional 48 records were identified through manual searches of reference lists from relevant studies and recent review articles on artificial intelligence in cardiovascular medicine. **Records Excluded: Records were screened and excluded manually by reviewers without the use of automation tools. After removing 700 duplicates, 2800 records were screened. Of these, 2470 were excluded for reasons including absence of AI application in cardiovascular disease prediction (n = 1600), reporting of only short-term or non-clinically relevant outcomes (n = 570), and publication in non-peer-reviewed sources such as conference abstracts, editorials, or commentaries (n = 300). These exclusions ensured that the final dataset remained focused on studies directly addressing artificial intelligence in cardiovascular risk prediction.

All retrieved records were exported to EndNote version 21 for reference management. After duplicate removal, full texts of potentially eligible studies were assessed. Ultimately, 48 studies met the inclusion criteria and were included in the final synthesis.

Quality Assessment

Methodological quality was evaluated using validated tools appropriate to study design. RCTs were assessed using the Cochrane Risk of Bias 2 (RoB 2) tool.¹⁷ Cohort studies were evaluated using the Newcastle–Ottawa Scale (NOS).¹⁸

All included studies were retained regardless of quality rating. Quality assessment informed interpretation through:

• Stratified reporting by risk-of-bias level (where feasible),
• Narrative discussion of methodological limitations,
• Cautious interpretation of findings from lower-quality studies.

Detailed quality appraisal results are presented in Supplementary Table S1.

Risk-of-Bias and Methodological Quality Assessment Summary

The majority of included RCTs were rated as having low risk of bias or some concerns using the Cochrane RoB 2 tool, primarily due to limitations in blinding of outcome assessment and incomplete reporting of model performance metrics. Cohort studies mostly received 7–9 stars on the NOS, indicating moderate-to-good methodological quality.

The quality assessment revealed three recurring critical limitations: (1) predominant reliance on internal validation with minimal external validation, (2) inconsistent or absent reporting of calibration metrics (despite frequent use of AUC), and (3) near absence of fairness assessment or subgroup analyses to detect algorithmic bias. These issues are discussed further in the narrative synthesis.

Data Extraction and Analysis

Data were extracted using a standardized form capturing study design, sample size, population characteristics, AI methodology, predictive performance metrics, and clinical outcomes.

Because of heterogeneity in populations, AI architectures, and endpoints, a narrative synthesis was conducted instead of meta-analysis. Findings were categorized into thematic domains, including risk prediction, imaging interpretation, and real-time monitoring. Descriptive statistics were employed to summarize methodological characteristics and performance trends. Any discrepancies during data extraction were resolved through consensus among reviewers.

Synthesis and Interpretation of Quantitative Findings

Owing to substantial heterogeneity across studies, quantitative improvements, such as increases in AUC or reductions in hospitalization rates, are reported as ranges from individual primary studies rather than pooled estimates. These values should be interpreted as indicative trends rather than definitive effect sizes. Greater interpretive weight was assigned to studies reporting comprehensive performance metrics and those with lower risk of bias.

Outcomes

The primary outcome of this umbrella review was the predictive performance of AI-based models, assessed mainly through discrimination AUC, calibration, and reclassification metrics, including net reclassification improvement and integrated discrimination improvement when reported.

Secondary outcomes comprised clinical endpoints such as hospitalization, mortality, emergency department visits, and other major cardiovascular events. These outcomes were summarized narratively without drawing causal inferences due to the observational and heterogeneous nature of most included studies.

In the narrative synthesis, findings from primary studies were prioritized, whereas secondary evidence was used solely to provide contextual pooled estimates without integration into the primary analyses.

Heterogeneity

Substantial heterogeneity was evident across the included studies in populations, data sources (electronic health records, imaging modalities, and wearable devices), AI architectures, validation strategies, and outcome definitions. This variability precluded quantitative meta-analysis and necessitates cautious interpretation of findings. Key contributors included demographic and clinical setting differences, multimodal data inputs, and predominant internal validation. These factors should be considered when evaluating generalizability.

Results

A total of 48 studies, including RCTs, 28 cohort studies, and 12 systematic reviews or meta-analyses, met predefined inclusion and quality criteria. Investigations were conducted within large collaborative programs in North America, Europe, and the Asia-Pacific region, with participant mean ages ranging from 50 to 80 years and female representation between 40% and 60%.

Reporting Characteristics of Included Prediction Models

To facilitate comparison of predictive performance across studies, key reporting characteristics of AI-based prediction models were summarized narratively, including validation approach (internal versus external), training and testing procedures (random split, chronological split, or cross-validation), calibration reporting alongside discrimination metrics, and assessment of fairness or algorithmic bias. Most studies relied exclusively on internal validation, with external validation—temporal or geographic—performed in only a small minority of investigations.^19–21 Calibration metrics were inconsistently reported, with most studies presenting discrimination via AUC without corresponding calibration assessment. Only a minority of studies reported comprehensive calibration evaluation, such as calibration plots, Hosmer–Lemeshow (HL) test, calibration slope or intercept, or expected calibration error (ECE).^{11,12,22–24}

Systematic fairness evaluation and subgroup analyses, stratified by race/ethnicity, sex, age, socioeconomic status, or geographic region, were rarely conducted, despite evidence that unmitigated algorithmic bias can substantially reduce predictive accuracy and lead to inequitable outcomes in minority, female, older, or socioeconomically disadvantaged populations.^25–27 Systematic fairness evaluation and subgroup validation therefore remain important priorities for future AI-based cardiovascular prediction research.^25–27

Heterogeneity and Sensitivity Considerations

Substantial clinical and methodological heterogeneity across studies precluded quantitative meta-analysis and necessitates cautious interpretation of performance estimates. Major sources of heterogeneity included differences in population characteristics, AI model architectures and feature selection, validation strategies, outcome definitions, and follow-up duration.

Imaging-based AI models generally demonstrated higher discrimination, with pooled AUC values frequently exceeding 0.90 in systematic reviews,²⁸ whereas electronic health record-based prediction models typically showed pooled AUC values between 0.82 and 0.88.¹³ Sensitivity analyses, when reported, indicated that model performance was generally stable across internal validation approaches but often declined in external or temporal validation cohorts.^11,21 These findings underscore the importance of external validation for reliable clinical translation.

These methodological variations significantly limit direct comparability of AUC values across studies and reduce confidence in the generalizability and reliability of the reported models. This prevailing focus on discrimination at the expense of calibration, validation, and fairness assessment constitutes a key gap in the translational readiness of AI models for CVD prediction.

Predictive Performance and Clinical Outcomes

AI techniques evaluated across studies included convolutional neural networks (CNNs), random forests (RFs), support vector machines (SVMs), and gradient boosting machines (GBMs). Across cohort studies and meta-analyses, AI-based models demonstrated predictive accuracy ranging from 80% to 95% in identifying individuals at elevated cardiovascular risk, depending on dataset characteristics and model architecture.^14,15 Imaging-based models generally achieved higher discrimination (pooled AUC ~0.91) compared with EHR-based models (pooled AUC ~0.86), with meta-analytic evidence reporting 12–25% improvements over conventional risk scores such as QResearch Risk Prediction Algorithm 3 (QRISK3).^28,29

Analyses of large-scale databases, including the UK Biobank AI initiatives (UK Biobank) and the EchoNet-Dynamic echocardiography study (EchoNet-Dynamic), supported improved identification of CAD, atrial fibrillation (AF), and HF-related hospitalization risk.^11,30 Cohort studies such as the Multi-Ethnic Study of Atherosclerosis (MESA) and the Cardiovascular Health Study (CHS) demonstrated that integrating AI into clinical workflows enhanced risk stratification, reduced diagnostic errors, and optimized cardiac imaging efficiency.^22,31 Evidence from controlled trials, including AI-electrocardiography approaches for AF detection and deep learning-based computed tomography angiography analysis, further confirmed strong predictive performance while illustrating trade-offs between model complexity, interpretability, and computational demands.^23,32

Studies in Asian populations, particularly in China and Japan, emphasized the integration of wearable technologies and reported greater performance gains in urban compared with rural healthcare settings.^20,33 These results suggest a consistent contribution of AI to cardiovascular risk prediction and clinical decision-making across diverse populations and care environments. However, gaps remain in calibration reporting, external validation, and fairness assessment. Mitigating these limitations through multicenter validation, explainable AI frameworks, comprehensive calibration evaluation, and fairness-aware reporting standards is essential for safe and equitable clinical implementation.

AI Applications in Cardiovascular Prediction

AI has been implemented across multiple clinical domains in cardiovascular disease, including risk stratification, diagnostic imaging, and real-time monitoring. Models integrating multimodal data sources such as electronic health records, imaging modalities, and wearable sensor data consistently outperformed single-modality approaches in detecting early subclinical disease signals.^14,15 Mitigation of potential bias in AI models, including race- or ethnicity-related bias, is essential for achieving equitable clinical outcomes. Evidence indicates that unaddressed bias can compromise predictive accuracy in underrepresented populations, underscoring the importance of robust external validation and fairness-aware algorithm development.²⁵ Furthermore, emerging observational evidence associates AI-driven monitoring strategies, including wearable-based detection systems, with reduced hospitalization rates for AF through earlier identification of arrhythmic events.³⁴ These models leverage large-scale datasets to improve risk classification while simultaneously revealing challenges in integration into established clinical workflows (Figure 2).

Figure 2 Diagrammatic illustration of AI’s contribution to cardiovascular risk forecasting, featuring multimodal data amalgamation, deployment challenges, and methods for effective incorporation into health service protocols.

Secondary evidence from meta-analyses supports these findings, demonstrating consistent improvements in predictive performance, including AUC gains of approximately 15–25% across studies, without overlapping primary data extraction.^12,29

Machine Learning in Risk Assessment

ML algorithms, including random forests and gradient boosting methods, have enhanced cardiovascular disease risk prediction by capturing nonlinear interactions among traditional risk factors. In cohort studies comparing AI-based models with conventional risk scores, ML models achieved AUC values ranging from 0.82 to 0.91, representing improvements of approximately 10–18% over traditional tools such as the FRS.^11,35 Figure 3 illustrates the analytical workflow through which ML models process complex datasets to improve predictive performance relative to conventional statistical approaches.

Figure 3 Diagrammatic overview of ML’s role in improving cardiovascular risk forecasting, depicting random forests and gradient boosting on varied data sources, leading to enhanced AUC scores and superior results over conventional systems.

Deep Learning for Cardiac Imaging

DL models, particularly CNNs, have markedly advanced cardiac imaging analysis by automating the interpretation of echocardiography, computed tomography (CT) angiography, and magnetic resonance imaging (MRI). Beyond anatomical evaluation, these models increasingly extract prognostic imaging biomarkers capable of forecasting future cardiovascular events. Reported diagnostic accuracy ranges from 85% to 95% for conditions such as left ventricular dysfunction (LVD) and CAD, while also facilitating prediction of incident HF, MI, and cardiovascular mortality through detection of subtle subclinical patterns not discernible via conventional interpretation.^23,28,36,37 These systems offer advantages in processing speed, reproducibility, and long-term risk stratification. Figure 4 depicts the workflow for DL-based imaging analysis and prediction.

Figure 4 Schematic depiction of DL framework in cardiac imaging automation, representing convolutional layers, pooling, and fully connected networks applied to input data, yielding 85–95% diagnostic precision for LV dysfunction and coronary lesions alongside expedited reporting.

Natural Language Processing in EHR Analysis

Natural language processing (NLP) methods enable extraction of clinically relevant information from unstructured EHR components, including physician notes and discharge summaries, capturing risk signals not available in structured data fields. When combined with structured EHR variables, NLP-enhanced models demonstrate improved discrimination for predicting MI and HF onset. Meta-analytic evidence indicates approximately 12–20% improvement in predictive accuracy, primarily measured by AUC, compared with traditional risk scores.¹² Specifically, predictive performance for MI improved in coronary artery disease populations,²⁹ while HF onset prediction, including hospital readmission risk, showed approximately 12–18% improvement.¹² These results underscore the broad applicability of NLP-based approaches across cardiovascular risk prediction tasks. Figure 5 depicts the integration of NLP-derived features with structured EHR data for enhanced prediction of MI and HF outcomes.

Figure 5 Schematic diagram of natural language processing application in electronic health record examination, demonstrating AI-assisted extraction from unstructured sources, risk pattern recognition, and resultant 12–20% elevated precision for ML prognosis alongside 12–18% better forecasting for heart failure admissions.

Personalized Medicine and AI in Cardiovascular Risk

AI has enabled the development of patient-specific risk models by integrating genetic, clinical, and lifestyle data to support individualized preventive strategies. Studies report an 18–25% improvement in calibration of individualized risk estimates compared with population-based risk scores, facilitating more precise therapeutic targeting in high-risk subgroups.^15,38 Figure 6 illustrates the key components of this approach, including predictive analytics for HF, genomic data integration, and wearable-based monitoring systems that enhance clinical decision-making.

Figure 6 Conceptual framework of artificial intelligence in personalized cardiology, featuring predictive modeling with convolutional neural networks yielding AUCs of 0.88 to 0.94 for HF prognosis, machine learning-enhanced polygenic risk scores improving coronary event forecasts by 20–30%, and wearable devices achieving AUCs of 0.90 to 0.96 alongside an 18–25% decrease in emergency room utilization.

Predictive Modeling for Heart Failure

ML and DL algorithms have been applied to predict HF onset and progression using multimodal inputs such as echocardiographic parameters and circulating biomarkers. These models illustrated AUC values ranging from 0.88 to 0.94 for prediction of one-year readmission risk, outperforming conventional prognostic scores by around 15–22% and enabling earlier clinical intervention.^30,39

Integration of Genomic Data

AI-based models that incorporate genomic variants alongside clinical risk profiles have further refined cardiovascular risk prediction. Polygenic risk scores enhanced through ML-based modeling improved prediction of coronary events by 20–30% beyond traditional clinical risk factors, with the greatest incremental value observed in younger populations.^38,40

Wearable Technology and Real-Time Monitoring

Wearable devices integrated with AI algorithms enable continuous monitoring of heart rate (HR) variability, physical activity patterns, and early arrhythmia detection. Reported sensitivity for AF detection ranged from 90% to 96%, while RCTs and cohort studies documented reductions in emergency department utilization ranging from 18% to 25% among individuals receiving proactive algorithm-driven alerts compared with usual care.^32,34 Table 1 summarizes the current evidence and practical applications of artificial intelligence in cardiovascular disease prediction.

Table 1 Key Applications of Artificial Intelligence in Cardiovascular Disease Prediction: a Stratified Overview of Landmark Studies

Discussion

This umbrella review evaluated the clinical applications and translational limitations AI in CVD risk prediction, with particular emphasis on real-world implementation rather than purely theoretical performance metrics. In addition to synthesizing existing evidence, this review proposes targeted strategies to address current barriers and promote interdisciplinary collaboration, thereby extending prior reviews that primarily focused on algorithmic performance.

The findings indicate that AI approaches, including ML, DL, and multimodal data integration, are consistently associated with improved predictive discrimination in cardiovascular risk assessment. More than 70% of imaging-based studies reported AUC values exceeding 0.90, and several individual studies described improvements in risk stratification ranging from approximately 15% to 25% compared with traditional tools such as the FRS and QRISK.^11,15 These improvements represent ranges reported across individual studies rather than pooled meta-analytic estimates and should therefore be interpreted as indicative rather than definitive.

Performance gains were particularly evident in real-time monitoring using wearable devices and in models integrating EHRs with genomic data, enabling earlier detection of AF, CAD, and HF across heterogeneous populations. One prospective study reported approximately 19% fewer unplanned cardiovascular hospitalizations with AI-assisted wearable monitoring compared with usual care;⁵¹ however, this observation derives from a single study and warrants cautious interpretation.

An important consideration for clinical translation is the uneven maturity of evidence across AI applications. Imaging-based AI models, including automated echocardiographic view classification, ejection fraction quantification, and coronary stenosis detection, have undergone extensive technical validation and, in some cases, prospective clinical evaluation.^20,23,28,46 Several tools have received regulatory clearance (eg, FDA or CE marking) and are primarily designed to enhance diagnostic accuracy and workflow efficiency, placing them closer to routine clinical adoption.^15,28 In contrast, multimodal and wearable-based AI models integrating EHR data, genomics, and continuous sensor inputs for long-term individualized risk prediction remain in a more exploratory phase.^12,15,38 Although these approaches hold substantial preventive potential, they frequently lack robust external validation across diverse populations, prospective trials demonstrating clinical utility, and clearly defined regulatory pathways.^19,25,33

Compared with prior high-impact publications, this umbrella review expands beyond analyses of deep learning (DL)-based cardiac imaging reported in Journal of the American Medical Association Cardiology (JAMA Cardiol)²⁸ by incorporating wearable technologies and polygenic risk scores (PRSs). By explicitly stratifying primary and secondary evidence, the present synthesis provides a more transparent comparison across levels of evidence. Additionally, studies from Asian cohorts reported up to 20% higher predictive performance in urban settings, potentially reflecting greater adoption of wearable technologies; these findings should be interpreted as context-specific observations rather than generalizable effects.

Whereas European Heart Journal (EHJ) meta-analyses have emphasized the limitations of traditional risk algorithms,⁹ the present synthesis underscores the capacity of AI models to capture nonlinear relationships within EHR data and unstructured clinical narratives through NLP, outperforming guideline-endorsed risk models by roughly 18–22% in multiethnic datasets.³⁰ Even more recent conventional scores, such as the PRECISE-HBR score for post-percutaneous coronary intervention (PCI) bleeding risk prediction, largely represent incremental improvements in discrimination within specific procedural contexts.¹⁰ By comparison, AI-based approaches offer broader multimodal integration, improved detection of nonlinear patterns, and adaptability across diverse cardiovascular conditions, representing a more fundamental methodological advancement.

Despite the promise of AI in precision medicine,¹⁵ clinical translation remains slower than anticipated.¹⁵ A central finding of this umbrella review is that progress has been uneven, with imaging-based tools approaching clinical readiness while multimodal and wearable-based models require further maturation in validation, interpretability, and regulatory pathways.

Limitations

The included studies showed substantial methodological heterogeneity in populations, AI algorithms, data sources, and outcome definitions, which precluded quantitative meta-analysis. The evidence is disproportionately focused on discrimination metrics (AUC), with comparatively less attention to other essential performance aspects.

Further limitations arise from restriction to English-language publications and datasets primarily from high-income countries, potentially restricting generalizability to low-resource settings and underrepresented populations. Additional barriers to clinical translation include data privacy considerations, the need for large high-quality datasets, variability in acute care delivery and provider behavior, and evolving regulatory requirements. These factors collectively underscore the need for rigorous multicenter validation, prospective implementation studies, and standardized reporting to facilitate safe and equitable adoption of AI-based cardiovascular risk prediction models.

Future Directions

Future research should prioritize large, multicenter randomized controlled trials using diverse longitudinal datasets to reduce selection and spectrum bias and improve global generalizability. Development of explainable AI (XAI) frameworks will be essential to enhance interpretability and clinician trust, notably in real-time clinical decision-support contexts. Health-economic analyses should also be incorporated to evaluate cost-effectiveness and equity in real-world implementation.

Federated learning architectures, together with adherence to reporting standards such as TRIPOD-AI, will be critical for accelerating regulatory approval, improving transparency, and facilitating clinical integration. Future studies synthesizing both primary and secondary evidence may further clarify the rapidly evolving landscape of AI applications in cardiovascular medicine. AI-based CVD prediction studies should routinely report both discrimination and comprehensive calibration metrics, including calibration plots, calibration-in-the-large statistics, and expected calibration error (ECE).

Conclusion

This umbrella review demonstrates that AI-based models show substantial promise in improving cardiovascular risk prediction, frequently achieving high discriminatory performance, particularly in imaging-based applications.

Bridging the gap between algorithmic performance and clinical implementation will require a fundamental shift in research priorities toward multicenter validation, explainable AI frameworks, comprehensive performance reporting, and equity-focused evaluation. A balanced evaluation framework that equally prioritizes accuracy, reliability, fairness, and clinical utility will be essential for the safe, effective, and equitable integration of AI into cardiovascular care.