The DOI matches its Crossref record; 191 of the 191 references it cites were found in a registry or an index.
Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review
Open accessAbstract
Background: A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. Methods: PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). Results: In 20,887 screened references, 79 articles (82.5% in 2017–2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development ( n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5–52,000, median 21) and large-span sample size (range 80–3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as “recommended”; however, 281 and 187 were “not recommended” and “warning,” respectively. Conclusion: AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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“Numerous studies have demonstrated that algorithms such as neural networks and gradient boosting machines exhibit superior discriminative ability over traditional methods in predicting cardiovascular complications in diabetes (6–8).”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [7]
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“A large proportion of studies rely on populations from Europe and North America, with inadequate representation of Asian and other non-Western groups, directly compromising clinical applicability in high-burden regions (6, 7).”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“A large proportion of studies rely on populations from Europe and North America, with inadequate representation of Asian and other non-Western groups, directly compromising clinical applicability in high-burden regions (6, 7).”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Despite excellent algorithmic results in many studies, systematic evaluations have revealed a high prevalence of risk of bias, primarily stemming from deficiencies in the participant selection domain, outcome measurement domain, and analysis domain (6, 7).”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“As a consequence, even models that perform exceptionally well on development datasets often show substantially lower or uncertain performance in external settings, highlighting a critical limitation in generalizability (7, 8).”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [7]
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“Furthermore, current models are predominantly developed using populations from Europe and North America, resulting in a critical lack of representativeness for Asian populations, where the burden of cardiovascular disease is particularly heavy.”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [7]
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Innovation and challenges of artificial intelligence technology in personalized healthcare
Li, Y.-H., Li, Y., Wei, M.-Y., & Li, G.-Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-70073-7 · Cited by 342
Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice
Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J., Wiens, J., Butte, A. J., & Topol, E. J. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.003 · Cited by 324
Liu, T., Krentz, A., Lu, L., & Curcin, V. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae080 · Cited by 177
DNA methylation in cardiovascular disease and heart failure: novel prediction models?
Desiderio, A., Pastorino, M., Campitelli, M., Longo, M., Miele, C., Napoli, R., Bèguinot, F., & Raciti, G. A. (2024) · Clinical Epigenetics · doi:10.1186/s13148-024-01722-x · Cited by 57
The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease
Chowdhury, M. A., Rizk, R., Chiu, C., Zhang, J., Scholl, J. L., Bosch, T. J., Singh, A. P., Baugh, L. A., McGough, J. S., Santosh, K., et al. (2025) · Biomedicines · doi:10.3390/biomedicines13020427 · Cited by 60
Metadata
- Authors
- Cai, Y., Cai, Y.-Q., Tang, L.-Y., Wang, Y.-H., Gong, M., Jing, T.-C., Li, H.-J., Li-Ling, J., Hu, W., Yin, Z., Gong, D.-X., & Zhang, G.-W.
- Affiliations
- China Medical University (CN)
- Digital China Health (China) (CN)
- First Hospital of China Medical University (CN)
- Corresponding author
- Yin, Zhihua
- Gong, Da-Xin
- Venue
- BMC Medicine
- Published
- 2024-02-05
- Volume
- 22
- Issue
- 1
- Pages
- 56-56
- ISSN
- 1741-7015 (ISSN-L)
- PMC
- PMC10845808
- OpenAlex
- W4391531696
- Cited by
- 122
- References
- 191
- Citations by year
- 2026: 62 · 2025: 45 · 2024: 15
- FWCI
- 9.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2020YFC2006406)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence, cardiovascular disease, risk prediction model, algorithmic transparency, risk of bias, PROBAST, clinical prediction tool, digital health
- MeSH terms
- Heart Disease Risk Factors, Prediction Methods, Machine, Africa, Algorithms, Artificial Intelligence, Cardiovascular Diseases, Europe, Humans, Mass Screening, Reproducibility of Results, Risk Assessment, Datasets as Topic
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