Background Mortality from cardiovascular disease (CVD) has seen a dramatic increase over the past decades, which has led to a significant increase in the development of risk prediction models. AI-based models have been proposed as a method of enhancing traditional risk models. This review aims to describe the present state of AI risk prediction models for cardiovascular disease in primary and ambulatory care research, and in particular to determine: the stage of development these models have reached, the AI approaches used, and identifying possible sources of bias or limitations in the AI models. Methods Using the Arksey and O Malley scoping review method, this review searched Pubmed, EBSCOHost, and Web of Science databases between 2019 and 2024, and relevant studies were identified. Data extraction was performed on eligible included studies. Results 22,860 studies were screened, and 25 articles were identified. There was a lack of external validation (20% of models) and lack of clinical impact studies (0%) in this review. A variety of AI techniques were used. Both data and algorithmic biases were commonly identified. There was a lack of geographic variation in datasets (60% were based in the USA) and only 32% of studies reported race and ethnicity data. There was poor predictor and outcome standardization. Calibration was only reported in 24% of models. Conclusion Findings from this review highlight the lack of clinical impact studies and risk of bias in current AI based models. It provides evidence for future refinement and development of AI risk prediction models in cardiovascular disease.

The authors have declared no competing interest.

This project was supported by UCD Summer Student Research Award and by Science Foundation Ireland under grant 21/FIP/SDG/9948

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