Integrating Artificial Intelligence into Cardiovascular Risk Prediction: A Comprehensive Review of Models, Predictors, and Limitations: A Review
Abstract
The use of artificial intelligence (AI) in cardiovascular disease (CVD) risk prediction has revolutionized preventive cardiology by improving diagnostic precision, early intervention, and health equity. The use of various datasets, including genomic, wearable, imaging, and electronic health records, is highlighted in this paper, which summarizes recent advancements in AI-based risk prediction models for CVD. The construction, creation, and validation of AI models are covered, with a focus on new predictors and how they affect model performance. The paper also examines the differences brought about by algorithmic bias, showing how underrepresentation of particular demographic groups can worsen health inequities and reduce predictive reliability. It is recognized that AI can perform better than conventional statistical models in some situations, especially when it comes to identifying at-risk persons and directing healthcare decisions. Nonetheless, there are still issues with the model's fairness, openness, and generalizability. In conclusion, even though AI has the potential to improve cardiovascular risk assessment and individualized treatment, thorough model evaluation and bias reduction techniques are essential to guaranteeing fair, dependable, and successful clinical application.
References
A. Mihan and H. G. C. Van Spall, “Interventions to enhance digital health equity in cardiovascular care,” Nat. Med., vol. 30, pp. 628–630, 2024.
E. D. Muse and E. J. Topol, “Transforming the cardiometabolic disease landscape: multimodal AI-powered approaches in prevention and management,” Cell Metab., vol. 36, pp. 670–683, 2024.
T. Averbuch et al., “Applications of artificial intelligence and machine learning in heart failure,” Eur. Heart J. Digit. Health, vol. 3, pp. 311–322, 2022.
A. D. Zahedani et al., “Digital health application integrating wearable data and behavioral patterns improves metabolic health,” NPJ Digit. Med., vol. 6, p. 216, 2023.
D. Vervoort et al., “Addressing the global burden of cardiovascular disease in women: JACC state-of-the-art review,” JACC, vol. 83, pp. 2690–2707, 2024.
L. Filbey et al., “Improving representativeness in trials: a call to action from the global cardiovascular clinical trialists forum,” Eur. Heart J., vol. 44, pp. 921–930, 2023.
L. H. Nazer et al., “Bias in artificial intelligence algorithms and recommendations for mitigation,” PLoS Digit. Health, vol. 2, p. e0000278, 2023.
K. N. Vokinger, S. Feuerriegel, and A. S. Kesselheim, “Mitigating bias in machine learning for medicine,” Commun. Med., vol. 1, no. 25, 2021.
M. Mittermaier, M. M. Raza, and J. C. Kvedar, “Bias in AI-based models for medical applications: challenges and mitigation strategies,” NPJ Digit. Med., vol. 6, no. 113, 2023.
World Health Organization CVD Risk Chart Working Group, “World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions,” Lancet Glob. Health, vol. 7, no. 10, pp. e1332–e1345, 2019.
J. A. Usher Smith et al., “Impact of provision of cardiovascular disease risk estimates to healthcare professionals and patients: a systematic review,” BMJ Open, vol. 5, no. 10, p. e008717, 2015.
H. Assadi et al., “The role of artificial intelligence in predicting outcomes by cardiovascular magnetic resonance: a comprehensive systematic review,” Medicina (Kaunas), vol. 58, no. 8, p. 1087, 2022.
T. Infante et al., “Radiogenomics and artificial intelligence approaches applied to cardiac CT angiography and cardiac MRI for precision medicine in coronary heart disease,” Circ. Cardiovasc. Imaging, vol. 14, no. 12, pp. 1133–1146, 2021.
A. Triantafyllidis et al., “Deep learning in mHealth for cardiovascular disease, diabetes, and cancer: systematic review,” JMIR Mhealth Uhealth, vol. 10, no. 4, p. e32344, 2022.
Y. Zhao et al., “Social determinants in machine learning cardiovascular disease prediction models: a systematic review,” Am. J. Prev. Med., vol. 61, no. 4, pp. 596–605, 2021.
S. Lee et al., “Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: a systematic review and meta-analysis,” Yonsei Med. J., vol. 63, no. 6 (Suppl), 2022.
O. T. Kee et al., “Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review,” Cardiovasc. Diabetol., vol. 22, no. 1, 2023.
C. L. Andaur Navarro et al., “Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review,” BMJ, vol. 20, 2021.
J. Fehr et al., “A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare,” Front. Digit. Health, vol. 6, 2024.
R. Liu et al., “An artificial intelligence-based risk prediction model of myocardial infarction,” BMC Bioinform., vol. 23, no. 1, 2022.
J. W. Hughes et al., “A deep learning-based ECG risk score for long-term cardiovascular death and disease,” NPJ Digit. Med., vol. 6, no. 1, 2023.
R. D. Riley et al., “Clinical prediction models and the multiverse of madness,” BMC Med., vol. 21, no. 1, 2023.
A. Banerjee et al., “Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review,” BMC Med., vol. 19, no. 1, 2021.
M. Khalifa and M. Albadawy, “Artificial intelligence for clinical prediction: exploring key domains and essential functions,” Comput. Methods Programs Biomed. Update, vol. 5, 100148, 2024.
H. Janes, M. S. Pepe, and W. Gu, “Assessing the value of risk predictions by using risk stratification tables,” Ann. Intern. Med., vol. 149, no. 10, pp. 751–760, 2008.
S. Bomrah et al., “A scoping review of machine learning for sepsis prediction—feature engineering strategies and model performance,” Crit. Care, vol. 28, no. 1, 2024.
D. M. Lloyd-Jones, “Cardiovascular risk prediction,” Circulation, vol. 121, no. 15, pp. 1768–1777, 2010.
R. D. Riley et al., “Clinical prediction models and the multiverse of madness,” BMC Med., vol. 18, no. 21, 2023.
N. Hassan et al., “Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making,” BMJ Health Care Inform., vol. 30, no. 1, 2023.
Y. Cai et al., “Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review,” BMC Med., vol. 22, no. 1, p. 56, 2024.
A. A. de Hond et al., “Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review,” NPJ Digit. Med., vol. 5, no. 1, 2022.
D. K. Arnett et al., “2019 ACC/AHA guideline on the primary prevention of cardiovascular disease,” Circulation, vol. 140, pp. e596–e646, 2019.
A. M. Navar et al., “Earlier treatment in adults with high lifetime risk of cardiovascular diseases,” Am. J. Prev. Cardiol., vol. 12, p. 100430, 2022.
D. A. Adedinsewo et al., “Cardiovascular disease screening in women: leveraging artificial intelligence and digital tools,” Circ. Res., vol. 130, pp. 673–690, 2022.
A. Atehortua et al., “Cardiometabolic risk estimation using exposome data and machine learning,” Int. J. Med. Inf., vol. 179, 105209, 2023.
M. W. Segar et al., “Development and validation of machine learning–based race-specific models to predict 10-year risk of heart failure,” Circulation, vol. 143, pp. 2370–2383, 2021.
M. W. Segar et al., “Incorporation of natriuretic peptides with clinical risk scores to predict heart failure among individuals with dysglycaemia,” Eur. J. Heart Fail., vol. 24, p. 2375, 2022.
L. D. Tavares et al., “Prediction of metabolic syndrome: a machine learning approach to help primary prevention,” Diabetes Res. Clin. Pract., 2022, p. 110047.
L. Pastika et al., “Artificial intelligence-enhanced electrocardiography derived BMI as a predictor of future cardiometabolic disease,” NPJ Digit. Med., vol. 7, no. 167, 2024.
R. Nadarajah et al., “Machine learning to identify community-dwelling individuals at higher risk of incident cardio-renal-metabolic diseases and death,” Future Health J., vol. 11, p. 100109, 2024.
J. J. Huang et al., “Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations,” NPJ Digit. Med., vol. 7, no. 196, 2024.
M. DeCamp and C. Lindvall, “Latent bias and the implementation of artificial intelligence in medicine,” J. Am. Med. Inform. Assoc., vol. 27, pp. 2020–2023, 2020.
D. Kaur et al., “Race, sex, and age disparities in the performance of ECG deep learning models predicting heart failure,” Circ. Heart Fail., vol. 17, e010879, 2024.
F. Li et al., “Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction,” J. Biomed. Inform., 104294, 2023.
C. Hong et al., “Predictive accuracy of stroke risk prediction models across Black and White race, sex, and age groups,” JAMA, 2023, p. 2022.24683.
Copyright (c) 2025 Khalid N. M Al-Khero, Mustafa Khalid Al-Kheroo, Haneen Bashar Hasan
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