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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.
In the citing paper
“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]
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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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Citations
Cites
Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019
Roth, G. A., Mensah, G. A., Johnson, C. O., Addolorato, G., Ammirati, E., Baddour, L. M., Barengo, N. C., Beaton, A. Z., Benjamin, E. J., Benziger, C. P., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.11.010 · Cited by 11,449
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
Roffi, M., Patrono, C., Collet, J.-P., Mueller, C., Valgimigli, M., Andreotti, F., Bax, J. J., Borger, M. A., Brotons, C., Chew, D. P., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehv320 · Cited by 8,261
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688
Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project
Conroy, R. (2003) · European Heart Journal · doi:10.1016/s0195-668x(03)00114-3 · Cited by 5,290
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Arnett, D. K., Blumenthal, R. S., Albert, M. A., Buroker, A. B., Goldberger, Z. D., Hahn, E. J., Himmelfarb, C. D., Khera, A., Lloyd-Jones, D., McEvoy, J. W., et al. (2019) · Circulation · doi:10.1161/cir.0000000000000678 · Cited by 4,536
(2019) · doi:10.1161/cir.0000000000000677 · Cited by 4,501
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · BMJ · doi:10.1136/bmj.g7594 · Cited by 4,388
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,319
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective
Mahmood, S. S., Levy, D., Vasan, R. S., & Wang, T. J. (2014) · The Lancet · doi:10.1016/s0140-6736(13)61752-3 · Cited by 1,622
Prognosis and prognostic research: validating a prognostic model
Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009) · BMJ · doi:10.1136/bmj.b605 · Cited by 1,436
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174944 · Cited by 1,346
Kaptoge, S. K., Pennells, L. A. M., Bacquer, D. D., Cooney, M. T., Kavousi, M., Stevens, G. A., Riley, L. M., Savin, S., Khan, T., Altay, S., et al. (2019) · The Lancet Global Health · doi:10.1016/s2214-109x(19)30318-3 · Cited by 1,245
European Society of Cardiology: cardiovascular disease statistics 2021
Timmis, A., Vardas, P. E., Townsend, N. P., Torbica, A., Katus, H. A., Smedt, D. D., Gale, C. P., Maggioni, A. P., Petersen, S. E., Huculeci, R., et al. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab892 · Cited by 1,235
Risk prediction models: II. External validation, model updating, and impact assessment
Moons, K. G. M., Kengne, A. P., Grobbee, D. E., Royston, P., Vergouwe, Y., Altman, D. G., & Woodward, M. (2012) · Heart · doi:10.1136/heartjnl-2011-301247 · Cited by 1,195
Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020) · BMJ · doi:10.1136/bmj.m689 · Cited by 1,152
Criteria for Evaluation of Novel Markers of Cardiovascular Risk
Hlatky, M. A., Greenland, P., Arnett, D. K., Ballantyne, C. M., Criqui, M. H., Elkind, M. S. V., Go, A. S., Harrell, F. E., Hong, Y., Howard, B. V., et al. (2009) · Circulation · doi:10.1161/circulationaha.109.192278 · Cited by 1,146
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01772-9 · Cited by 1,039
Genetic analyses of diverse populations improves discovery for complex traits
Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., Highland, H. M., Patel, Y. M., Sorokin, E. P., Avery, C. L., et al. (2019) · Nature · doi:10.1038/s41586-019-1310-4 · Cited by 1,033
Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5870 · Cited by 988
Prediction models for cardiovascular disease risk in the general population: systematic review
Damen, J. A. A. G., Hooft, L., Schuit, E., Debray, T. P. A., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C. M., Chiocchia, V., Roberts, C., et al. (2016) · BMJ · doi:10.1136/bmj.i2416 · Cited by 935
Prognosis and prognostic research: application and impact of prognostic models in clinical practice
Moons, K. G. M., Altman, D. G., Vergouwe, Y., & Royston, P. (2009) · BMJ · doi:10.1136/bmj.b606 · Cited by 934
Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-048008 · Cited by 852
A guide to systematic review and meta-analysis of prognostic factor studies
Riley, R. D., Moons, K. G. M., Snell, K. I. E., Ensor, J., Hooft, L., Altman, D. G., Hayden, J. A., Collins, P. G. S., & Debray, T. P. A. (2019) · BMJ · doi:10.1136/bmj.k4597 · Cited by 771
Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., & Denniston, A. K. (2020) · BMJ · doi:10.1136/bmj.m3164 · Cited by 702
Reporting of artificial intelligence prediction models
Collins, G. S., & Moons, K. G. M. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30037-6 · Cited by 701
Motwani, M., Dey, D., Berman, D. S., Germano, G., Achenbach, S., Al-Mallah, M. H., Andreini, D., Budoff, M. J., Cademartiri, F., Callister, T. Q., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw188 · Cited by 692
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.
Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1041-y · Cited by 665 · cited 3×
“…calculators that can allow batch processing of participant information (e.g., a prediction software or tool) ∙ APPRAISE‑AI [31] ∙ MI‑CLAIM [32] ∙ AI‑TREE [33] II Apply and report the classic algorithms that can be found in some common tools/platforms OR report complete codes and hyperpa‑…”
BackgroundAlaa, A. M., Bolton, T. R., Angelantonio, E. D., Rudd, J. H. F., & van der Schaar, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213653 · Cited by 652
Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis
Ambale-Venkatesh, B., Yang, X., Wu, C. O., Liu, K., Hundley, W. G., McClelland, R., Gomes, A. S., Folsom, A. R., Shea, S., Guallar, E., et al. (2017) · Circulation Research · doi:10.1161/circresaha.117.311312 · Cited by 639
Fairness of artificial intelligence in healthcare: review and recommendations
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607
Logistic regression was as good as machine learning for predicting major chronic diseases
Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., Wong, T. Y., & Cheng, C. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.002 · Cited by 597
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106848 · Cited by 581
Vollmer, S. J., Mateen, B. A., Bohner, G., Király, F. J., Ghani, R., Jonsson, P. F., Cumbers, S., Jonas, A., McAllister, K., Myles, P. R., et al. (2020) · BMJ · doi:10.1136/bmj.l6927 · Cited by 574
Steyerberg, E. W., Borsboom, G. J. J. M., van Houwelingen, H. C., Eijkemans, M. J. C., & Habbema, J. D. F. (2004) · Statistics in Medicine · doi:10.1002/sim.1844 · Cited by 552
Collins, G. S., Mallett, S., Omar, O., & Yu, L.-M. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-103 · Cited by 484
Kocak, B., Baessler, B., Bakas, S., Cuocolo, R., Fedorov, A., Maier-Hein, L., Mercaldo, N., Müller, H., Orlhac, F., Pinto dos Santos, D., et al. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01415-8 · Cited by 471
Rivera, S. C., Liu, X., Chan, A.-W., Denniston, A. K., & Calvert, M. J. (2020) · BMJ · doi:10.1136/bmj.m3210 · Cited by 463
Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ · doi:10.1136/bmj.n2281 · Cited by 415
Hernandez-Boussard, T., Bozkurt, S., Ioannidis, J. P. A., & Shah, N. H. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa088 · Cited by 410
Updating methods improved the performance of a clinical prediction model in new patients
Janssen, K. J. M., Moons, K. G. M., Kalkman, C. J., Grobbee, D. E., & Vergouwe, Y. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.04.018 · Cited by 395
Artificial intelligence in dental research: Checklist for authors, reviewers, readers
Schwendicke, F., Singh, T., Lee, J., Gaudin, R., Chaurasia, A., Wiegand, T., Uribe, S. E., & Krois, J. (2021) · Journal of Dentistry · doi:10.1016/j.jdent.2021.103610 · Cited by 394
Prediction Modeling Using EHR Data
Wu, J., Roy, J. A., & Stewart, W. F. (2010) · Medical Care · doi:10.1097/mlr.0b013e3181de9e17 · Cited by 381
Gaziano, T. A., Young, C. R., Fitzmaurice, G., Atwood, S., & Gaziano, J. M. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60418-3 · Cited by 366
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M., & Chau, D. H. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030418 · Cited by 362
Systematic review of prognostic models in traumatic brain injury
Perel, P., Edwards, P. J., Wentz, R., & Roberts, I. (2006) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-6-38 · Cited by 351
Karimian, G., Petelos, E., & Evers, S. (2022) · AI and Ethics · doi:10.1007/s43681-021-00131-7 · Cited by 328
Comparisons of established risk prediction models for cardiovascular disease: systematic review
Siontis, G. C. M., Tzoulaki, I., Siontis, K. C., & Ioannidis, J. P. A. (2012) · BMJ · doi:10.1136/bmj.e3318 · Cited by 300
Balki, I., Amirabadi, A., Levman, J., Martel, A. L., Emersic, Z., Meden, B., Garcia-Pedrero, A., Ramirez, S. C., Kong, D., Moody, A. R., et al. (2019) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2019.06.002 · Cited by 299
An efficient convolutional neural network for coronary heart disease prediction
Dutta, A., Batabyal, T., Basu, M., & Acton, S. T. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113408 · Cited by 290
DOME: recommendations for supervised machine learning validation in biology
Walsh, I., Fishman, D., Garcia-Gasulla, D., Titma, T., Pollastri, G., ELIXIR Machine Learning Focus Group, Capriotti, E., Casadio, R., Capella-Gutierrez, S., Cirillo, D., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01205-4 · Cited by 282
van de Sande, D., van Genderen, M. E., Huiskens, J., Gommers, D., & van Bommel, J. (2021) · Intensive Care Medicine · doi:10.1007/s00134-021-06446-7 · Cited by 279
Sounderajah, V., Ashrafian, H., Aggarwal, R., De Fauw, J., Denniston, A. K., Greaves, F., Karthikesalingam, A., King, D., Liu, X., Markar, S. R., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0941-1 · Cited by 270
Recommendations for Reporting Machine Learning Analyses in Clinical Research
Stevens, L. M., Mortazavi, B. J., Deo, R. C., Curtis, L., & Kao, D. P. (2020) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.120.006556 · Cited by 268
Sengupta, P. P., Shrestha, S., Berthon, B., Messas, E., Donal, E., Tison, G. H., Min, J. K., D’hooge, J., Voigt, J., Dudley, J. T., et al. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2020.07.015 · Cited by 230
Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA
Kakadiaris, I. A., Vrigkas, M., Yen, A. A., Kuznetsova, T., Budoff, M., & Naghavi, M. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.009476 · Cited by 227
Zhao, J., Feng, Q., Wu, P., Lupu, R. A., Wilke, R. A., Wells, Q. S., Denny, J. C., & Wei, W. (2019) · Scientific Reports · doi:10.1038/s41598-018-36745-x · Cited by 223
Predictive analytics in health care: how can we know it works?
Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz130 · Cited by 223
Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning
Betancur, J., Otaki, Y., Motwani, M., Fish, M., Lemley, M., Dey, D., Gransar, H., Tamarappoo, B., Germano, G., Sharir, T., et al. (2017) · JACC Cardiovascular Imaging · doi:10.1016/j.jcmg.2017.07.024 · Cited by 220
Validation of Biomarker-based risk prediction models
Taylor, J. M. G., Ankerst, D., & Andridge, R. (2008) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-07-4534 · Cited by 207
Azmi, J., Arif, M., Nafis, M. T., Alam, M. A., Tanweer, S., & Wang, G. (2022) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2022.103825 · Cited by 200
Al‐Zaiti, S. S., Besomi, L., Bouzid, Z., Faramand, Z., Frisch, S. O., Martin‐Gill, C., Gregg, R. E., Saba, S., Callaway, C. W., & Sejdić, E. (2020) · Nature Communications · doi:10.1038/s41467-020-17804-2 · Cited by 198
Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S., Wessels, L. F. A., Hafner, M., Sharan, R., Peng, J., et al. (2021) · Nature Cancer · doi:10.1038/s43018-020-00169-2 · Cited by 180
Sheridan, S., Pignone, M., & Mulrow, C. (2003) · Journal of General Internal Medicine · doi:10.1111/j.1525-1497.2003.30107.x · Cited by 178
Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology
Daneshjou, R., Barata, C., Betz‐Stablein, B., Celebi, M. E., Codella, N., Combalia, M., Guitera, P., Gutman, D. A., Halpern, A. C., Helba, B., et al. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.4915 · Cited by 166
Akazawa, M., & Hashimoto, K. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102164 · Cited by 164
APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support
Kwong, J. C. C., Khondker, A., Lajkosz, K., McDermott, M. B. A., Borrat, X., McCradden, M. D., Mamdani, M., Kulkarni, G. S., & Johnson, A. E. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.35377 · Cited by 159
Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease
van Smeden, M., Heinze, G., CALSTER, B. V., Asselbergs, F. W., Vardas, P. E., Bruining, N., de Jaegere, P. P. T., Moore, J. H., Denaxas, S., Boulesteix, A., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac238 · Cited by 155
Commandeur, F., Slomka, P. J., Goeller, M., Chen, X., Cadet, S. J., Razipour, A., McElhinney, P. A., Gransar, H., Cantú, S. M., Miller, R. J. H., et al. (2019) · Cardiovascular Research · doi:10.1093/cvr/cvz321 · Cited by 153
Mezzatesta, S., Torino, C., Meo, P. D., Fiumara, G., & Vilasi, A. D. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.05.005 · Cited by 151
Voß, R., Cullen, P., Schulte, H., & Assmann, G. (2002) · International Journal of Epidemiology · doi:10.1093/ije/31.6.1253 · Cited by 148
Li, Y., Sperrin, M., Ashcroft, D. M., & van Staa, T. P. (2020) · BMJ · doi:10.1136/bmj.m3919 · Cited by 148
Olczak, J., Pavlopoulos, J., Prijs, J., IJpma, F. F. A., Doornberg, J. N., Lundström, C. F., Hedlund, J., & Gordon, M. (2021) · Acta Orthopaedica · doi:10.1080/17453674.2021.1918389 · Cited by 145
Machine Learning in Vascular Surgery: A Systematic Review and Critical Appraisal
Li, B., Feridooni, T., Cuen-Ojeda, C., Kishibe, T., de Mestral, C., Mamdani, M., & Al-Omran, M. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00552-y · Cited by 145
Clinician checklist for assessing suitability of machine learning applications in healthcare
Scott, I. A., Carter, S. M., & Coiera, E. W. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100251 · Cited by 143
Cardiovascular risk assessment: a global perspective
Zhao, D. H., Liu, J., Xie, W., & Qi, Y. (2015) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2015.28 · Cited by 143
Navarro, C. L. A., Damen, J. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, P. G. S., Bajpai, R. C., Riley, R. D., Moons, K. G. M., et al. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01469-6 · Cited by 142
Considering Biased Data as Informative Artifacts in AI-Assisted Health Care
Ferryman, K. S., Mackintosh, M. E., & Ghassemi, M. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2214964 · Cited by 142
Tiwari, P., Colborn, K. L., Smith, D. E., Xing, F., Ghosh, D., & Rosenberg, M. A. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.19396 · Cited by 138
Forcier, M. B., Gallois, H., Mullan, S. M., & Joly, Y. (2019) · Journal of Law and the Biosciences · doi:10.1093/jlb/lsz013 · Cited by 135
Stroke Prediction with Machine Learning Methods among Older Chinese
Wu, Y., & Fang, Y. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17061828 · Cited by 135
Debray, T. P. A., Riley, R. D., Rovers, M. M., Reitsma, J. B., Moons, K. G. M., & Cochrane IPD Meta-analysis Methods group (2015) · PLoS Medicine · doi:10.1371/journal.pmed.1001886 · Cited by 134
Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation
Christopoulos, G., Graff‐Radford, J., Lopez, C., Yao, X., Attia, Z. I., Rabinstein, A. A., Petersen, R. C., Knopman, D. S., Mielke, M. M., Kremers, W. K., et al. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009355 · Cited by 134
Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention
Hoogeveen, R. M., Pereira, J. P. B., Nurmohamed, N. S., Zampoleri, V., Bom, M. J., Baragetti, A., Boekholdt, S. M., Knaapen, P., Khaw, K., Wareham, N. J., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa648 · Cited by 133
Prognostic Models: A Methodological Framework and Review of Models for Breast Cancer
Altman, D. G. (2009) · Cancer Investigation · doi:10.1080/07357900802572110 · Cited by 129
Rasmy, L., Wu, Y., Wang, N., Geng, X., Zheng, W. J., Wang, F., Wu, H., Xu, H., & Zhi, D. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.06.011 · Cited by 127
Mylrea, M., & Robinson, N. (2023) · Entropy · doi:10.3390/e25101429 · Cited by 127
Segar, M. W., Jaeger, B. C., Patel, K. V., Nambi, V., Ndumele, C. E., Correa, A., Butler, J., Chandra, A., Ayers, C. R., Rao, S. G., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.053134 · Cited by 126
Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk
Dimopoulos, A. C., Νικολαϊδου, Μ., Caballero, F. F., Engchuan, W., Niubo, A. S., Arndt, H., Ayuso‐Mateos, J. L., Haro, J. M., Chatterji, S., Georgousopoulou, E. N., et al. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0644-1 · Cited by 118
Jha, A. K., Bradshaw, T., Buvat, I., Hatt, M., Prabhat, K. C., Liu, C., Obuchowski, N., Saboury, B., Slomka, P. J., Sunderland, J. J., et al. (2022) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.263239 · Cited by 117
Prediction Models — Development, Evaluation, and Clinical Application
Pencina, M. J., Goldstein, B. A., & D’Agostino, R. B. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2000589 · Cited by 116
Ward, A., Sarraju, A., Chung, S., Li, J., Harrington, R., Heidenreich, P., Palaniappan, L., Scheinker, D., & Rodriguez, F. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00331-1 · Cited by 112
Comparing risk prediction models
Collins, G. S., & Moons, K. G. M. (2012) · BMJ · doi:10.1136/bmj.e3186 · Cited by 110
Du, Z., Yang, Y., Zheng, J., Li, Q., Lin, D., Li, Y., Fan, J., Cheng, W., Chen, X.-H., & Cai, Y. (2020) · JMIR Medical Informatics · doi:10.2196/17257 · Cited by 107
Self-citationMachine learning to predict cardiovascular risk
Quesada, J. A., López‐Pineda, A., Gil-Guillén, V. F., Durazo-Arvizú, R., Orozco‐Beltrán, D., López-Domenech, A., & Carratalá‐Munuera, C. (2019) · International Journal of Clinical Practice · doi:10.1111/ijcp.13389 · Cited by 103
Machine learning adds to clinical and CAC assessments in predicting 10-year CHD and CVD deaths
Nakanishi, R., Slomka, P., Rios, R., Betancur, J., Blaha, M., Nasir, K., Miedema, M., Rumberger, J., Gransar, H., Shaw, L., et al. (2020) · JACC Cardiovascular Imaging · doi:10.1016/j.jcmg.2020.08.024 · Cited by 96
Zhao, Y., Wood, E. P., Mirin, N., Vedanthan, R., Cook, S., & Chunara, R. (2020) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2021.04.016 · Cited by 93
Zarkogianni, K., Αθανασίου, Μ., Thanopoulou, A. C., & Nikita, K. S. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2765639 · Cited by 92
Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine
Currie, G. M., & Hawk, K. E. (2020) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2020.08.001 · Cited by 92
Liu, W., Laranjo, L., Klimis, H., Chiang, J., Yue, J., Marschner, S. L., Quiroz, J. C., Jorm, L. R., & Chow, C. K. (2023) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcad017 · Cited by 91
Chun, M., Clarke, R., Cairns, B. J., Clifton, D. A., Bennett, D., Chen, Y., Guo, Y., Pei, P., Lv, J., Yu, C., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab068 · Cited by 89
Pre-existing and machine learning-based models for cardiovascular risk prediction
Cho, S.-Y., Kim, S., Kang, S., Lee, K. J., Choi, D., Kang, S., Park, S. J., Kim, T., Yoon, C., Youn, T., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-88257-w · Cited by 88
The assessment list for trustworthy artificial intelligence: A review and recommendations
Radclyffe, C. J. T., Ribeiro, M., & Wortham, R. H. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1020592 · Cited by 88
Jamthikar, A. D., Gupta, D., Saba, L., Khanna, N. N., Araki, T., Višković, K., Mavrogeni, S. I., Laird, J. R., Pareek, G., Miner, M. M., et al. (2020) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2020.01.07 · Cited by 85
Joo, G., Song, Y., Im, H., & Park, J. (2020) · IEEE Access · doi:10.1109/access.2020.3015757 · Cited by 84
Colombet, I., Ruelland, A., Châtellier, G., Gueyffier, F., Patrice Degoulet, A., & Jaulent, M. (2000) · PubMed Central · Cited by 83
Banerjee, A., Chen, S., Fatemifar, G., Zeina, M., Lumbers, R. T., Mielke, J., Gill, S. K., Kotecha, D., Freitag, D. F., Denaxas, S., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-01940-7 · Cited by 80
Zhang, P.-I., Hsu, C., Kao, Y., Chen, C., Kuo, Y.-W., Hsu, S., Liu, T.-L., Lin, H., Wang, J., Liu, C., et al. (2020) · Scandinavian Journal of Trauma Resuscitation and Emergency Medicine · doi:10.1186/s13049-020-00786-x · Cited by 79
Usher‐Smith, J. A., Šilarova, B., Schuit, E., Moons, K. G. M., & Griffin, S. J. (2015) · BMJ Open · doi:10.1136/bmjopen-2015-008717 · Cited by 78
Qureshi, N. Q., Mufarrih, S. H., Bloomfield, G. S., Tariq, W., Almas, A., Mokdad, A. H., Bartlett, J. A., Nisar, M. I., Siddiqi, S., Bhutta, Z. A., et al. (2021) · Global Heart · doi:10.5334/gh.815 · Cited by 74
Meta‐analysis and aggregation of multiple published prediction models
Debray, T. P. A., Koffijberg, H., Nieboer, D., Vergouwe, Y., Steyerberg, E. W., & Moons, K. G. M. (2014) · Statistics in Medicine · doi:10.1002/sim.6080 · Cited by 73
WHO and ITU establish benchmarking process for artificial intelligence in health
Wiegand, T., Krishnamurthy, R., Kuglitsch, M. M., Lee, N., Pujari, S., Salathé, M., Wenzel, M. A., & Xu, S. Z. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30762-7 · Cited by 72
Gallifant, J., Zhang, J., del Pilar Arias Lopez, M., Zhu, T., Camporota, L., Celi, L. A., & Formenti, F. (2022) · British Journal of Anaesthesia · doi:10.1016/j.bja.2021.09.025 · Cited by 72
The AI life cycle: a holistic approach to creating ethical AI for health decisions
Ng, M. Y., Kapur, S., Blizinsky, K. D., & Hernandez-Boussard, T. (2022) · Nature Medicine · doi:10.1038/s41591-022-01993-y · Cited by 71
Suzuki, S. S., Yamashita, T., Sakama, T., Arita, T., Yagi, N., Otsuka, T., Semba, H., Kano, H., Matsuno, S., Kato, Y., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221911 · Cited by 69
Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals
Sánchez‐Cabo, F., Rosselló, X., Fuster, V., Benito, F., Manzano, J. P., Silla-Castro, J. C., Fernández-Alvira, J. M., Oliva, B., Fernández‐Friera, L., López‐Melgar, B., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.08.017 · Cited by 67
Lin, A., Wong, N. D., Razipour, A., McElhinney, P. A., Commandeur, F., Cadet, S. J., Gransar, H., Chen, X., Cantú, S. M., Miller, R. J. H., et al. (2021) · Cardiovascular Diabetology · doi:10.1186/s12933-021-01220-x · Cited by 67
Validation framework for the use of AI in healthcare: overview of the new British standard BS30440
Sujan, M. A., Smith-Frazer, C., Malamateniou, C., Connor, J., Gardner, A., Unsworth, H., & Husain, H. (2023) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100749 · Cited by 67
Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes
Nowak, C., Carlsson, A. C., Östgren, C. J., Nyström, F. H., Alam, M., Feldreich, T., Sundström, J., Carrero, J. J., Leppert, J., Hedberg, P., et al. (2018) · Diabetologia · doi:10.1007/s00125-018-4641-z · Cited by 65
Klement, W., & Emam, K. E. (2023) · Journal of Medical Internet Research · doi:10.2196/48763 · Cited by 65
Ricciardi, C., Edmunds, K. J., Recenti, M., Sigurdsson, S. O., Gudnason, V. G., Carraro, U., & Gargiulo, P. (2020) · Scientific Reports · doi:10.1038/s41598-020-59873-9 · Cited by 64
Jiang, Y., Zhang, X., Ma, R., Wang, X., Liu, J., Keerman, M., Yan, Y., Ma, J., Song, Y., Zhang, J., et al. (2021) · Clinical Epidemiology · doi:10.2147/clep.s313343 · Cited by 63
Development of Health Parameter Model for Risk Prediction of CVD Using SVM
Unnikrishnan, P., Kumar, D., Arjunan, S. P., Kumar, H., Mitchell, P., & Kawasaki, R. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/3016245 · Cited by 62
Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework
Tanguay, W., Acar, P., Fine, B., Abdolell, M., Gong, B., Cadrin-Chênevert, A., Chartrand‐Lefebvre, C., Chalaoui, J., Gorgos, A. B., Chin, A. S.-L., et al. (2022) · Canadian Association of Radiologists Journal · doi:10.1177/08465371221135760 · Cited by 62
A New Hybrid XGBSVM Model: Application for Hypertensive Heart Disease
Chang, W., Liu, Y., Wu, X., Xiao, Y., Zhou, S., & Cao, W. (2019) · IEEE Access · doi:10.1109/access.2019.2957367 · Cited by 59
Peiris, D., Ghosh, A., Manne‐Goehler, J., Jaacks, L. M., Theilmann, M., Marcus, M. E., Zhumadilov, Z. S., Tsabedze, L., Supiyev, A., Bahendeka, S., et al. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003485 · Cited by 59
Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review
Triantafyllidis, A. K., Kondylakis, H., Katehakis, D. G., Kouroubali, A., Koumakis, L., Kostas Marias, A., Alexiadis, A., Votis, K., & Tzovaras, D. K. (2022) · JMIR mhealth and uhealth · doi:10.2196/32344 · Cited by 57
MAIC–10 brief quality checklist for publications using artificial intelligence and medical images
Cerdá-Alberich, L., Solana, J., Mallol, P., Ribas, G., García-Junco, M., Alberich‐Bayarri, Á., & Martí‐Bonmatí, L. (2023) · Insights into Imaging · doi:10.1186/s13244-022-01355-9 · Cited by 56
Lindholm, D. P., Fukaya, E., Leeper, N. J., & Ingelsson, E. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.008970 · Cited by 55
Suri, J. S., Bhagawati, M., Paul, S., Protogerou, A. D., Sfikakis, P. P., Kitas, G. D., Khanna, N. N., Ruzsa, Z., Sharma, A. M., Saxena, S., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030722 · Cited by 55
The AIMe registry for artificial intelligence in biomedical research
Matschinske, J., Alcaraz, N., Benis, A., Golebiewski, M., Grimm, D. G., Heumos, L., Kacprowski, T., Lazareva, O., List, M., Louadi, Z., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01241-0 · Cited by 54
Orfanoudaki, A., Chesley, E., Cadisch, C., Stein, B. D., Nouh, A. M., Alberts, M. J., & Bertsimas, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232414 · Cited by 53
Infante, T., Cavaliere, C., Punzo, B., Grimaldi, V., Salvatore, M., & Claudio Napoli, A. (2021) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.121.013025 · Cited by 52
Prediction for cardiovascular diseases based on laboratory data: An analysis of random forest model
Su, X., Xu, Y., Tan, Z., Wang, X., Yang, P., Ya-ni, S., Jiang, Y., Qin, S., & Shang, L. (2020) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.23421 · Cited by 50
Bouzid, Z., Faramand, Z., Gregg, R. E., Frisch, S. O., Martin‐Gill, C., Saba, S., Callaway, C. W., Sejdić, E., & Al‐Zaiti, S. S. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.017871 · Cited by 49
Machine Learning for Health: Algorithm Auditing & Quality Control
Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., Goldschmidt, P. G., Johner, C., Schörverth, E. D. M., Nakasi, R., et al. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01783-y · Cited by 48
Okser, S., Lehtimäki, T. J., Elo, L. L., Mononen, N., Peltonen, N., Kähönen, M. A. P., Juonala, M., Fan, Y., Hernesniemi, J. A., Laitinen, T. P., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001146 · Cited by 47
Lacson, R. C., Baker, B., Suresh, H., Andriole, K. P., Szolovits, P., & Lacson, E. (2018) · Clinical Kidney Journal · doi:10.1093/ckj/sfy049 · Cited by 47
Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction
Kim, J., Kang, U., & Lee, Y. (2017) · Healthcare Informatics Research · doi:10.4258/hir.2017.23.3.169 · Cited by 46
Best practices for authors of healthcare-related artificial intelligence manuscripts
Kakarmath, S., Esteva, A., Arnaout, R., Harvey, H., Kumar, S., Muse, E., Dong, F., Wedlund, L., & Kvedar, J. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00336-w · Cited by 46
Solares, J. R. A., Canoy, D., Raimondi, F. E. D., Zhu, Y., Hassaïne, A., Salimi‐Khorshidi, G., Tran, J., Copland, E., Zottoli, M., Pinho‐Gomes, A., et al. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.012129 · Cited by 44
Pan, Z., Zhang, R., Shen, S., Lin, Y., Zhang, L., Wang, X., Ye, Q., Wang, X., Chen, J., Zhao, Y., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104443 · Cited by 44
Bundy, J. D., Heckbert, S. R., Chen, L. Y., Lloyd‐Jones, D. M., & Greenland, P. (2019) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2019.09.032 · Cited by 43
Mandair, D., Tiwari, P., Simon, S., Colborn, K. L., & Rosenberg, M. A. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01268-x · Cited by 42
Dogan, M. V., Beach, S. R. H., Simons, R. L., Lendasse, A., Penaluna, B. K., & Philibert, R. A. (2018) · Genes · doi:10.3390/genes9120641 · Cited by 41
Priyanga, P., Pattankar, V. V., & Sridevi, S. (2020) · Computational Intelligence · doi:10.1111/coin.12405 · Cited by 41
Machine learning for risk prediction of acute coronary syndrome.
VanHouten, J. P., Starmer, J. M., Lorenzi, N. M., Maron, D. J., & Lasko, T. A. (2014) · PubMed Central · Cited by 41
Han, D., Kolli, K. K., Gransar, H., Lee, J. H., Choi, S.-Y., Chun, E. J., Han, H.-W., Park, S. H., Sung, J., Jung, H. O., et al. (2020) · Journal of Cardiovascular Computed Tomography · doi:10.1016/j.jcct.2019.09.005 · Cited by 41
Dalakleidi, K. V., Zarkogianni, K., Thanopoulou, A. C., & Nikita, K. S. (2017) · Expert Systems · doi:10.1111/exsy.12214 · Cited by 41
Tesche, C., Bauer, M., Baquet, M., Hedels, B., Straube, F., Hartl, S., Gray, H. N., Jochheim, D., Aschauer, T., Rogowski, S., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07083-2 · Cited by 40
Novel Approaches for Predicting Risk Factors of Atherosclerosis
Rao, V. S. H., & Kumar, M. N. (2012) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/titb.2012.2227271 · Cited by 39
de Gonzalo‐Calvo, D., Martínez‐Camblor, P., Bär, C., Duarte, K., Girerd, N., Fellström, B., Schmieder, R. E., Jardine, A. G., Massy, Z. A., Holdaas, H., et al. (2020) · Theranostics · doi:10.7150/thno.46123 · Cited by 39
Development and verification of prediction models for preventing cardiovascular diseases
Sung, J. M., Cho, I., Sung, D., Kim, S., Kim, H. C., Chae, M.-H., Kavousi, M., Rueda-Ochoa, O. L., Ikram, M. A., Franco, O. H., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222809 · Cited by 37
Lee, A. K., Katz, R., Jotwani, V., Garimella, P. S., Ambrosius, W. T., Cheung, A. K., Gren, L. H., Neyra, J. A., Punzi, H. A., Raphael, K. L., et al. (2019) · Hypertension · doi:10.1161/hypertensionaha.119.13339 · Cited by 37
Sajeev, S., Champion, S., Beleigoli, A. M. R., Chew, D. P., Reed, R. L., Magliano, D. J., Shaw, J. E., Milne, R. L., Appleton, S., Gill, T. K., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18063187 · Cited by 37
Carresi, C., Scicchitano, M., Scarano, F., Macrì, R., Bosco, F., Nucera, S., Ruga, S., Zito, M. C., Mollace, R., Guarnieri, L., et al. (2021) · Nutrients · doi:10.3390/nu13010275 · Cited by 33
Johri, A. M., Mantella, L. E., Jamthikar, A. D., Saba, L., Laird, J. R., & Suri, J. S. (2021) · International journal of cardiac imaging · doi:10.1007/s10554-021-02294-0 · Cited by 33
A novel neural-inspired learning algorithm with application to clinical risk prediction
Tay, D., Poh, C. L., & Kitney, R. I. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.12.014 · Cited by 32
Goldstein, B. A., Chang, T. I., Mitani, A., Assimes, T. L., & Winkelmayer, W. C. (2013) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.03050313 · Cited by 31
Raghu, A., Praveen, D., Peiris, D., Tarassenko, L., & Clifford, G. D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133618 · Cited by 30
Lennerz, J. K., Salgado, R., Kim, G. E., Sirintrapun, S. J., Thierauf, J. C., Singh, A., Indave, I., Bard, A., Weissinger, S. E., Heher, Y. K., et al. (2023) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2022-1151 · Cited by 29
A machine-learning approach to cardiovascular risk prediction in psoriatic arthritis
Navarini, L., Sperti, M., Currado, D., Costa, L., Deriu, M. A., Margiotta, D. P. E., Tasso, M., Scarpa, R., Afeltra, A., & Caso, F. (2019) · Rheumatology · doi:10.1093/rheumatology/kez677 · Cited by 29
Dehghan, A., Rayatinejad, A., Khezri, R., Aune, D., & Rezaei, F. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-01961-1 · Cited by 28
Akyea, R. K., Leonardi‐Bee, J., Asselbergs, F. W., Patel, R. S., Durrington, P. N., Wierzbicki, A. S., Ibiwoye, O. H., Kai, J., Qureshi, N., & Weng, S. F. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-034564 · Cited by 26
Nuñez, J.-J., Nguyen, T. T., Zhou, Y., Cao, B., Ng, R. T., Chen, J., Frey, B. N., Milev, R. V., Müller, D. J., Rotzinger, S., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253023 · Cited by 26
Andy, A., Guntuku, S. C., Adusumalli, S., Asch, D. A., Groeneveld, P. W., Ungar, L., & Merchant, R. M. (2021) · JMIR Cardio · doi:10.2196/24473 · Cited by 23
Apostolopoulos, I. D., & Groumpos, P. P. (2020) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2020.1768534 · Cited by 22
Alfieri, F., Ancona, A., Tripepi, G. L., Randazzo, V., Paviglianiti, A., Pasero, E. G. A., Vecchi, L., Politi, C., Cauda, V., & Fagugli, R. M. (2022) · Journal of Nephrology · doi:10.1007/s40620-022-01335-8 · Cited by 22
Puddu, P. E., & Menotti, A. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-100 · Cited by 21
Standardization of Artificial Intelligence Development in Radiotherapy
de Biase, A., Sourlos, N., & van Ooijen, P. M. A. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2022.06.010 · Cited by 20
Kim, I., Yang, P., Jang, E., Jung, H., You, S. C., Yu, H. T., Kim, T., Uhm, J., Pak, H., Lee, M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-73537-8 · Cited by 18
Ngufor, C. G., Caraballo, P. J., O'Byrne, T. J., Chen, D. J., Shah, N. D., Pruinelli, L., Steinbach, M. S., & Simon, G. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.8270 · Cited by 16
Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events
Schrempf, M., Kramer, D., Jauk, S., Veeranki, S. P. K., Leodolter, W., & Rainer, P. P. (2021) · Studies in health technology and informatics · doi:10.3233/shti210100 · Cited by 16
Added Value of a Resting ECG Neural Network That Predicts Cardiovascular Mortality
Perez, M. V., Dewey, F. E., Tan, S. Y., MYERS, J. N., & Froelicher, V. F. (2009) · Annals of Noninvasive Electrocardiology · doi:10.1111/j.1542-474x.2008.00270.x · Cited by 14
Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality
Bello, G., Dumancas, G. G., & Gennings, C. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s30172 · Cited by 13
Grout, R. W., Hui, S. L., Imler, T. D., El‐Azab, S. A., Baker, J., Sands, G. H., Ateya, M., & Pike, F. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01482-1 · Cited by 13
Assadi, H. S., Alabed, S., Maiter, A., Salehi, M., Li, R., Ripley, D. P., van der Geest, R. J., Zhong, Y., Zhong, L., Swift, A. J., et al. (2022) · Medicina · doi:10.3390/medicina58081087 · Cited by 13
Cho, I., Sung, J. M., Kim, H. C., Lee, S., Chae, M.-H., Kavousi, M., Rueda-Ochoa, O. L., Ikram, M. A., Franco, O. H., Min, J. K., et al. (2019) · Korean Circulation Journal · doi:10.4070/kcj.2019.0105 · Cited by 8
Zhang, Y., Han, Y., Gao, P., Mo, Y., Hao, S., Huang, J., Ye, F., Li, Z., Zheng, L., Yao, X., et al. (2021) · JMIR Medical Informatics · doi:10.2196/23606 · Cited by 8
Hong, D., Fort, D. G., Shi, L., & Price‐Haywood, E. G. (2021) · Diabetes Therapy · doi:10.1007/s13300-021-01096-w · Cited by 7
Ogata, K., Miyamoto, T., Adachi, H., Hirai, Y., Enomoto, M., Fukami, A., Yokoi, K., Kasahara, A., Tsukagawa, E., Yoshimura, A., et al. (2013) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2012.12.023 · Cited by 7
Rare Disease in Cardiovascular Medicine I
Akhtar, M. M., & Elliot, P. M. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx241 · Cited by 5
Rare Diseases in Cardiovascular Medicine II
Akhtar, M. M., & Elliot, P. M. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx242 · Cited by 5
Assessing the Performance of the HAS-BLED Score
Corbanese, U. (2011) · CHEST Journal · doi:10.1378/chest.10-2995 · Cited by 5
Why Canada’s Artificial Intelligence and Data Act Needs “Mental Data”
White, D. J., & Skorburg, J. A. (2023) · AJOB Neuroscience · doi:10.1080/21507740.2023.2188302 · Cited by 3
Research works printed but not linked: 2
- Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, Cumbers S, Jonas A, McAllister KSL, Myles P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;368:l6927. https://doi.org/10.1136/bmj.l6927. · DOI
- Directorate-General for Communications Networks, Content and Technology (2019) Ethics guidelines for trustworthy AI https://www.data.europa.eu/doi/10.2759/177365. · DOI
Cited by
Sorted by citations
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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