A scoping review on artificial intelligence-based tools for cardiovascular disease risk prediction.

Background

Cardiovascular disease (CVD) is a leading cause of death worldwide, making early risk prediction essential for improving outcomes. Although artificial intelligence (AI) models promise to improve predictions, questions remain about interpretability, the reliability of risk factors, and the need for cross-validation. This scoping review examined the extent, types, and reporting quality of studies that used AI-based prognostic models to predict CVD risk in individuals without established CVD.

Methods

A systematic search of PubMed, IEEE Xplore, Web of Science, Scopus, and Google Scholar identified 6,710 records. Screening and data extraction followed the PRISMA-ScR framework and JBI Guidelines for Scoping Reviews. Included studies were evaluated against the TRIPOD-AI reporting checklist. The review protocol was registered with the Open Science Framework (OSF: https://osf.io/8nq6s/).

Results

Thirty studies met the inclusion criteria; all published after 2017. Most studies utilized existing models on large datasets, predominantly leveraging unimodal clinical data and established machine learning algorithms such as Random Forests and Support Vector Machines. Twenty-four of the 30 studies used unimodal approaches, and the six multimodal studies demonstrated consistently strong performance, but they rest on a small, heterogeneous set of studies. Twelve studies conducted direct comparisons with traditional risk scores such as the Framingham Risk Score, showing comparable or modestly improved discrimination, although methodological heterogeneity limits the strength of these conclusions. External validation was reported in only seven studies, and calibration (agreement between the predicted and observed event rates) was not reported in any of the 30 included studies. Sensitivity, which determines a model's ability to identify truly high-risk individuals, was the least reported metric, appearing in only four studies, and no study reported a decision curve analysis.

Conclusion

AI-based CVD risk prediction tools show promise but have critical gaps in validation, calibration reporting, and clinical utility assessment, which currently preclude clinical deployment. Future research should prioritize external validation across diverse populations, mandatory reporting of calibration and sensitivity alongside discrimination metrics, as well as the adoption of reporting standards and appraisal tools such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD + AI) or Prediction Model Risk of Bias Assessment Tool + Artificial Intelligence (PROBAST + AI) to improve transparency and reproducibility.