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While EHR\u2010based CVD risk prediction has greatly improved, moving from models that integrate real\u2010world data on medication use and imaging, challenges persist regarding data quality, standardization across health care systems, and geographic variability. The complexity of EHR data requires sophisticated computational methods and multidisciplinary approaches for effective CVD risk modeling. AI's deep learning enhances prediction performance but faces limitations in interpretability and the need for validation and recalibration for diverse populations. The future of CVD risk prediction and management increasingly depends on using EHR and AI technologies effectively. Addressing data quality issues and overcoming limitations from retrospective data analysis are critical for improving the reliability and applicability of risk prediction models. Integrating multidimensional data, including environmental, lifestyle, social, and genomic factors, could significantly enhance risk assessment. These models require continuous validation and recalibration to ensure their adaptability to diverse populations and evolving health care environments, providing reassurance about their reliability.<\/jats:p>","DOI":"10.1161\/jaha.124.036946","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T06:34:12Z","timestamp":1741847652000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":43,"title":["Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review"],"prefix":"10.1161","author":[{"given":"Ming\u2010Lung","family":"Tsai","sequence":"first","affiliation":[{"name":"New Taipei Municipal Tucheng Hospital","place":["New Taipei, Taiwan"]},{"name":"Chang Gung University","place":["Taoyuan, Taiwan"]},{"name":"Chang Gung University","place":["Taoyuan, 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