ARTIFICIAL INTELLIGENCE IN CARDIOVASCULAR DISEASE PREDICTION: A COMPREHENSIVE INTEGRATIVE FRAMEWORK FROM MULTIMODAL METHODOLOGIES TO CLINICAL IMPLEMENTATION
DOI:
https://doi.org/10.31435/ijitss.1(49).2026.5245Keywords:
Artificial Intelligence, Cardiovascular Disease, Risk Prediction, Machine Learning, Implementation Science, External ValidationAbstract
Background: Cardiovascular diseases remain the leading cause of morbidity and mortality world-wide. Traditional risk scores, although widely used, rely on a limited number of linearly weighted variables and may perform poorly in heterogeneous populations. The growing availability of multimodal data, including electronic health records, imaging, wearable-device data, and omics, has increased interest in artificial intelligence (AI) for more individualized cardiovascular risk prediction.
Objective: This narrative review examines the current role of AI in cardiovascular disease prediction, with particular attention to model performance, methodological limitations, validation standards, and barriers to clinical implementation.
Methods: A structured narrative review of peer‑reviewed literature published up to December 2025 was conducted. Targeted searches in PubMed, IEEE Xplore, and Scopus were supplemented by queries of clinical trial registries and regulatory/health‑technology assessment sources. The synthesis prioritizes external validation, calibration, implementation science, health‑economic evaluation, and equity considerations.
Results: Recent evidence suggests that machine learning and deep learning models may achieve better discrimination than conventional clinical risk scores, with pooled area under the curve values often reported in the range of 0.85 to 0.87. However, the literature is marked by substantial heterogeneity, inconsistent calibration reporting, limited external validation, and underuse of time-to-event modeling approaches. These weaknesses reduce reproducibility and hinder safe translation into routine practice.
Conclusions: AI has considerable potential to improve cardiovascular risk prediction, but clinical adoption requires stronger methodological rigor, prospective validation, recalibration strategies, trans-parent reporting, and evaluation of ethical, organizational, and economic consequences.
References
Abimbola, S., Patel, B., Peiris, D., Patel, A., Harris, M., Usherwood, T., & Greenhalgh, T. (2019). The NASSS framework for ex post theorisation of technology-supported change in healthcare: Worked example of the TORPEDO programme. BMC Medicine, 17(1), 58. https://doi.org/10.1186/s12916-019-1463-x
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., Lett, E., Menon, B. K., Sheehan, K. A., & Al-Zaiti, S. S. (2024). Use of artificial intelligence in improving outcomes in heart disease: A scientific statement from the American Heart Association. Circulation, 149(14), e1028–e1050. https://doi.org/10.1161/CIR.0000000000001201
Cai, Y., Cai, Y.-Q., Tang, L.-Y., Wang, Y.-H., Gong, M., Jing, T.-C., Li, H.-J., Hu, W., Zhang, Z.-W., Zhang, X., & Zhang, G.-W. (2024a). Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: A systematic review. BMC Medicine, 22(1), 56. https://doi.org/10.1186/s12916-024-03273-7
Cai, Y.-Q., Gong, D.-X., Tang, L.-Y., Cai, Y., Li, H.-J., Jing, T.-C., Gong, M., Hu, W., Zhang, Z.-W., Zhang, X., & Zhang, G.-W. (2024b). Pitfalls in developing machine learning models for predicting cardiovascular diseases: Challenge and solutions. Journal of Medical Internet Research, 26, e47645. https://doi.org/10.2196/47645
Davis, S. E., Greevy, R. A., Jr., Lasko, T. A., Walsh, C. G., & Matheny, M. E. (2020). Detection of calibration drift in clinical prediction models to inform model updating. Journal of Biomedical Informatics, 112, 103611. https://doi.org/10.1016/j.jbi.2020.103611
Desai, M. Y., Jadam, S., Abusafia, M., Rutkowski, K., Ospina, S., Gaballa, A., Sultana, S., Thamilarasan, M., Xu, B., & Popovic, Z. B. (2025). Real-world artificial intelligence-based electrocardiographic analysis to diagnose hypertrophic cardiomyopathy. JACC: Clinical Electrophysiology, 11(6), 1324–1333. https://doi.org/10.1016/j.jacep.2025.02.024
Lewontin, M., Kaplan, E., Bilchick, K. C., Barber, A., Bivona, D., Kramer, C. M., Parrish, A., McClean, K., Thomas, M., Perry, A., Amos, K., & Ayers, M. (2025). Advanced diagnosis of hypertrophic cardiomyopathy with AI-ECG and differences based on ethnicity and HCM subtype. Journal of Clinical Medicine, 14(13), 4718. https://doi.org/10.3390/jcm14134718
Thao, V., Zhu, Y., Tseng, A. S., Inselman, J. W., Borah, B. J., McCoy, R. G., Attia, Z. I., Lopez-Jimenez, F., Pellikka, P. A., Rushlow, D. R., Friedman, P. A., Noseworthy, P. A., & Yao, X. (2024). Cost-effectiveness of artificial intelligence-enabled electrocardiograms for early detection of low ejection fraction: A secondary analysis of the electrocardiogram artificial intelligence-guided screening for low ejection fraction trial. Mayo Clinic Proceedings: Digital Health, 2(4), 620–631. https://doi.org/10.1016/j.mcpdig.2024.10.001
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017). Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), e367. https://doi.org/10.2196/jmir.8775
Krones, F., & Walker, B. (2024). From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings. PLOS Digital Health, 3(12), e0000437. https://doi.org/10.1371/journal.pdig.0000437
Liu, T., Krentz, A. J., Lu, L., & Curcin, V. (2025). Machine learning based prediction models for cardiovascular disease risk using electronic health records data: Systematic review and meta-analysis. European Heart Journal - Digital Health, 6(1), 7–22. https://doi.org/10.1093/ehjdh/ztae080
Luu, J. H., Borisenko, E., Przekop, V., Patil, A., Forrester, J. D., & Choi, J. (2024). Practical guide to building machine learning-based clinical prediction models using imbalanced datasets. Trauma Surgery & Acute Care Open, 9(1), e001222. https://doi.org/10.1136/tsaco-2023-001222
Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., Molling, P. E., Krien, J. S., Foss, R. M., Barry, B. A., Siontis, K. C., Kapa, S., Pellikka, P. A., Lopez-Jimenez, F., Attia, Z. I., ... Noseworthy, P. A. (2021). Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: A pragmatic, randomized clinical trial. Nature Medicine, 27(5), 815–819. https://doi.org/10.1038/s41591-021-01335-4
Meder, B., Asselbergs, F. W., & Ashley, E. (2025). Artificial intelligence to improve cardiovascular population health. European Heart Journal, 46(20), 1907–1916. https://doi.org/10.1093/eurheartj/ehaf125
Patel, D., Kantamneni, R., John, J. D., Patel, T., Shukla, A., Salma, A., & Anand, N. (2025). Artificial intelligence in cardiology: An updated systematic review with ethical considerations and challenges in implementing artificial intelligence models. Annals of Medicine, 57(1), 2415132. https://doi.org/10.1080/07853890.2024.2415132
Sufian, M. A., Alsadder, L., Hamzi, W., Zaman, S., Sagar, A. S. M. S., & Hamzi, B. (2024). Mitigating algorithmic bias in AI-driven cardiovascular imaging for fairer diagnostics. Diagnostics, 14(23), 2675. https://doi.org/10.3390/diagnostics14232675
Teshale, A. B., Htun, H. L., Vered, M., Owen, A. J., & Freak-Poli, R. (2024). A systematic review of artificial intelligence models for time-to-event outcome applied in cardiovascular disease risk prediction. Journal of Medical Systems, 48(1), 68. https://doi.org/10.1007/s10916-024-02087-7
Van Calster, B., McLernon, D. J., van Smeden, M., Wynants, L., & Steyerberg, E. W. (2019). Calibration: The Achilles heel of predictive analytics. BMC Medicine, 17(1), 230. https://doi.org/10.1186/s12916-019-1466-7
Raghunath, A., Nguyen, D. D., Schram, M., Albert, D., Gollakota, S., Shapiro, L., & Sridhar, A. R. (2023). Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation. Cardiovascular Digital Health Journal, 4(1), 21–28. https://doi.org/10.1016/j.cvdhj.2023.01.002
Wu, W.-T., Chao, Y.-W., Lin, T.-K., Huang, C.-K., & Hsieh, P.-H. (2025). Economic evaluation of AI-assisted technologies in healthcare: A systematic review. Journal of Food and Drug Analysis, 33(4), 487–500. https://doi.org/10.38212/2224-6614.3570
Yang, J., & Guan, J. (2022). A heart disease prediction model based on feature optimization and Smote-Xgboost algorithm. Information, 13(10), 475. https://doi.org/10.3390/info13100475
Yang, J. C. (2022). The prediction and analysis of heart disease using XGBoost algorithm. In 2022 IEEE International Conference on Information Technology and Engineering (ICITE) (pp. 151–156). https://doi.org/10.1109/ICITE56321.2022.10047570
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Copyright (c) 2026 Maciej Tomasz Wieczorek, Jeremi Leon Jasiński, Karolina Julia Hak, Alicja Maria Mitan, Aleksandra Maria Tomaszewska, Weronika Napierała, Kamila Teresa Kańska, Karolina Magda Leszczyńska, Anna Krzysztofik, Karolina Krawczyk
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