Artificial intelligence and machine learning in cardiovascular medicine: current applications, clinical evidence, and future directions

Abstract

Cardiovascular disease remains the leading cause of global mortality with nearly 19 million deaths annually, while exponential growth in multimodal imaging, continuous monitoring, and electronic health record data has created analytical challenges exceeding traditional methods. This narrative review examines artificial intelligence (AI) and machine learning (ML) applications in cardiovascular medicine through a comprehensive literature analysis from 2019 to 2025, focusing on clinical validation and regulatory approvals. Current applications demonstrate significant clinical utility: automated ECG interpretation achieves > 90% accuracy in arrhythmia detection and predicts life-threatening arrhythmias up to two weeks before clinical onset; deep learning cardiac imaging analysis matches expert performance while reducing analysis time from 45 minutes to under 5 minutes; and ML risk prediction outperforms traditional scores with area under the curve values of 0.865 vs. 0.765. The FDA has approved 122 cardiology AI algorithms representing 14% of all clinical AI in the U.S. market, including ECG interpretation, echocardiographic measurement, and imaging analysis systems. Real-world implementations demonstrate 15–25% reductions in diagnostic errors and 20–30% faster emergency intervention times. However, substantial challenges persist: data quality limitations, algorithmic bias with 10–15% performance variation across populations, workflow integration barriers, and validation requirements. Future directions include multimodal systems, continuously learning algorithms, precision medicine applications, and equitable global implementation. Successful integration requires addressing limitations through diverse training datasets, transparent development, standardized validation, provider education, and maintaining physician autonomy. AI and ML represent powerful augmentation tools that, through evidence-based implementation, can transform cardiovascular care while preserving clinical expertise in decision-making.

Keywords

artificial intelligence / machine learning / cardiovascular medicine / deep learning / clinical decision support / cardiac imaging / risk stratification

References

[1] Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al.; GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020; 76: 2982-3021.

[2] Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB Sr, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014; 63: 2935-59.

[3] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521: 436-44.

[4] Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019; 25: 70-4.

[5] Ouyang D, He B, Ghorbani A, Yuan N, Ebinger J, Langlotz CP, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020; 580: 252-6.

[6] Ethics and governance of artificial intelligence for health [Internet]. WHO; c2026 [cited 2021 Jun 18]. Available from: https://www.who.int/publications/i/item/9789240029200

[7] Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019; 25: 65-9.

[8] Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018; 71: 2668-79.

[9] Ribeiro AH, Ribeiro MH, Paixão GMM, Oliveira DM, Gomes PR, Canazart JA, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020; 11: 1760.

[10] Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, et al. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke. Circulation. 2021; 143: 1287-98.

[11] Krumholz HM. Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff (Millwood). 2014; 33: 1163-70.

[12] Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation. 2018; 138: 1623-35.

[13] Ghorbani A, Ouyang D, Abid A, He B, Chen JH, Harrington RA, et al. Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020; 3: 10.

[14] Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol. 2016; 68: 2287-95.

[15] Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun. 2021; 12: 715.

[16] Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018; 20: 65.

[17] Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, et al. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study. JACC Cardiovasc Imaging. 2018; 11: 1654-63.

[18] Liu T, Krentz A, Lu L, Curcin V. Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis. Eur Heart J Digit Health. 2024; 6: 7-22.

[19] Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017; 12: e0174944.

[20] Koehler F, Koehler K, Deckwart O, Prescher S, Wegscheider K, Kirwan BA, et al. Efficacy of telemedical interventional management in patients with heart failure (TIM-HF2): a randomised, controlled, parallel-group, unmasked trial. Lancet. 2018; 392: 1047-57.

[21] Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, et al. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circ Cardiovasc Qual Outcomes. 2016; 9: 629-40.

[22] Cikes M, Sanchez-Martinez S, Claggett B, Duchateau N, Piella G, Butakoff C, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. 2019; 21: 74-85.

[23] Ahmad T, Lund LH, Rao P, Ghosh R, Warier P, Vaccaro B, et al. Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients. J Am Heart Assoc. 2018; 7: e008081.

[24] Goto S, Kimura M, Katsumata Y, Goto S, Kamatani T, Ichihara G, et al. Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients. PLoS One. 2019; 14: e0210103.

[25] Pipilis A, Andrikopoulos G, Lekakis J, Kalantzi K, Kitsiou A, Toli K, et al.; HELIOS group. Outcome of patients with acute myocardial infarction admitted in hospitals with or without catheterization laboratory: results from the HELIOS registry. Eur J Cardiovasc Prev Rehabil. 2009; 16: 85-90.

[26] Liu N, Lin Z, Cao J, Koh Z, Zhang T, Huang GB, et al. An intelligent scoring system and its application to cardiac arrest prediction. IEEE Trans Inf Technol Biomed. 2012; 16: 1324-31.

[27] Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci. 2021; 14: 86-93.

[28] Shah RU, Freeman JV, Shilane D, Wang PJ, Go AS, Hlatky MA. Procedural complications, rehospitalizations, and repeat procedures after catheter ablation for atrial fibrillation. J Am Coll Cardiol. 2012; 59: 143-9.

[29] Benedetto U, Dimagli A, Sinha S, Cocomello L, Gibbison B, Caputo M, et al. Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis. J Thorac Cardiovasc Surg. 2022; 163: 2075-87.e9.

[30] Kilic A, Goyal A, Miller JK, Gleason TG, Dubrawksi A. Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement. Ann Thorac Surg. 2021; 111: 503-10.

[31] Seetharam K, Shrestha S, Sengupta PP. Artificial Intelligence in Cardiovascular Medicine. Curr Treat Options Cardiovasc Med. 2019; 21: 25.

[32] Adolf R, Nano N, Chami A, von Schacky CE, Will A, Hendrich E, et al. Convolutional neural networks on risk stratification of patients with suspected coronary artery disease undergoing coronary computed tomography angiography. Int J Cardiovasc Imaging. 2023; 39: 1209-16.

[33] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997; 9: 1735-80.

[34] Tison GH, Zhang J, Delling FN, Deo RC. Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery. Circ Cardiovasc Qual Outcomes. 2019; 12: e005289.

[35] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. arXiv [Preprint]. 2023 [cited 2023 Aug 2]. Available from: https://doi.org/10.48550/arXiv.1706.03762

[36] Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, et al. Grand challenges in clinical decision support. J Biomed Inform. 2008; 41: 387-92.

[37] Sana F, Isselbacher EM, Singh JP, Heist EK, Pathik B, Armoundas AA. Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review. J Am Coll Cardiol. 2020; 75: 1582-92.

[38] Adler-Milstein J, Jha AK. HITECH Act Drove Large Gains In Hospital Electronic Health Record Adoption. Health Aff (Millwood). 2017; 36: 1416-22.

[39] Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine. 2020; 37: 50-60.

[40] Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digital Medicine. 2020; 3: 118.

[41] Gerke S, Minssen T, Cohen G. Chapter 12 - Ethical and legal challenges of artificial intelligence-driven healthcare. In: Bohr A, Memarzadeh K, editors. Artificial Intelligence in Healthcare. Academic Press; 2020. pp. 295-336.

[42] Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019; 17: 195.

[43] Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020; 368: m689.

[44] Park SH, Han K. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. Radiology. 2018; 286: 800-9.

[45] Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018; 1: 18.

[46] Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013; 20: 144-51.

[47] Krumholz HM, Terry SF, Waldstreicher J. Data Acquisition, Curation, and Use for a Continuously Learning Health System. JAMA. 2016; 316: 1669-70.

[48] Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019; 366: 447-53.

[49] Adamson AS, Smith A. Machine Learning and Health Care Disparities in Dermatology. JAMA Dermatol. 2018; 154: 1247-8.

[50] Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical Machine Learning in Healthcare. Annu Rev Biomed Data Sci. 2021; 4: 123-44.

[51] Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv [Preprint]. 2017 [cited 2017 Mar 2]. Available from: https://arxiv.org/abs/1702.08608

[52] Adadi A, Berrada M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access. 2018; 6: 52138-60.

[53] Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015; 350: g7594.

[54] Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health. 2020; 2: e537-48.

[55] Chen JH, Asch SM. Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. N Engl J Med. 2017; 376: 2507-9.

[56] Hardy A, Hardy M, Kochenderfer M, Lamparth M, Reuel A, Smith C. BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices. In: Advances in Neural Information Processing Systems 37. 2024 Dec 10-15; Vancouver, Canad. Neural Information Processing Systems Foundation, Inc. (NeurIPS); 2024. pp. 21763-1813.

[57] Masters K. Artificial intelligence in medical education. Med Teach. 2019; 41: 976-80.

[58] Wartman SA, Combs CD. Reimagining Medical Education in the Age of AI. AMA J Ethics. 2019; 21: E146-52.

[59] Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018; 15: e1002689.

[60] Price WN 2nd, Gerke S, Cohen IG. Potential Liability for Physicians Using Artificial Intelligence. JAMA. 2019; 322: 1765-6.

[61] Char DS, Abràmoff MD, Feudtner C. Identifying Ethical Considerations for Machine Learning Healthcare Applications. Am J Bioeth. 2020; 20: 7-17.

[62] Shortliffe EH. Artificial Intelligence in Medicine: Weighing the Accomplishments, Hype, and Promise. Yearb Med Inform. 2019; 28: 257-62.

[63] McDougall RJ. Computer knows best? The need for value-flexibility in medical AI. J Med Ethics. 2019; 45: 156-60.

[64] Bjerring JC, Busch J. Artificial intelligence and patient-centered decision-making. Philos Technol. 2021; 34: 349-71.

[65] Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022; 28: 31-8.

[66] Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019; 25: 24-9.

[67] Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery; 2016. pp. 785-94.

[68] Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25: 44-56.

[69] Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018; 2: 719-31.

[70] Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019; 6: 94-8.

[71] Sendak MP, Gao M, Brajer N, Balu S. Presenting machine learning model information to clinical end users with model facts labels. NPJ Digit Med. 2020; 3: 41.

[72] Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc. 2001; 8: 527-34.

[73] Liu Y, Chen PC, Krause J, Peng L. How to Read Articles That Use Machine Learning: Users’ Guides to the Medical Literature. JAMA. 2019; 322: 1806-16.

[74] Price II WN, Gerke S, Cohen IG. Liability for use of artificial intelligence in medicine. In: Solaiman B, Cohen IG, editors. Research Handbook on Health, AI and the Law. Cheltenham, UK: Edward Elgar Publishing Ltd; 2024. Chapter 9.

[75] Kvedar J, Coye MJ, Everett W. Connected health: a review of technologies and strategies to improve patient care with telemedicine and telehealth. Health Aff (Millwood). 2014; 33: 194-9.

[76] Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018; 378: 981-3.

[77] Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018; 2: 158-64.

[78] Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, et al. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circ Arrhythm Electrophysiol. 2020; 13: e008437.

[79] Brownstein JS, Freifeld CC, Madoff LC. Digital Disease Detection - Harnessing the Web for Public Health Surveillance. N Engl J Med. 2009; 360: 2153-7.

[80] Leiner T, Rueckert D, Suinesiaputra A, Baeßler B, Nezafat R, Išgum I, et al. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson. 2019; 21: 61.

[81] Commandeur F, Slomka PJ, Goeller M, Chen X, Cadet S, Razipour A, et al. Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study. Cardiovasc Res. 2020; 116: 2216-25.

[82] Kalimouttou A, Kennedy JN, Feng J, Singh H, Saria S, Angus DC, et al. Optimal Vasopressin Initiation in Septic Shock: The OVISS Reinforcement Learning Study. JAMA. 2025; 333: 1688-98.

[83] Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016; 3: 160035.

[84] Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, et al. Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches. JAMA Cardiol. 2017; 2: 204-9.

[85] Samad MD, Ulloa A, Wehner GJ, Jing L, Hartzel D, Good CW, et al. Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning. JACC Cardiovasc Imaging. 2019; 12: 681-9.

[86] Krumholz HM, Curry LA, Bradley EH. Survival after acute myocardial infarction (SAMI) study: the design and implementation of a positive deviance study. Am Heart J. 2011; 162: 981-7.e9.

[87] Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA. 2018; 319: 1317-8.

[88] Whelton PK, Carey RM, Aronow WS, Casey DE Jr, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2018; 71: e127-248.

[89] Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2. Cambridge, MA, USA; MIT Press. 2014. pp. 2672-80.

[90] Rasheed A, San O, Kvamsdal T. Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access. 2020; 8: 21980-2012.

[91] Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S. Quantum machine learning. Nature. 2017; 549: 195-202.

[92] Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health. 2021; 3: e195-203.

[93] Meskó B, Hetényi G, Győrffy Z. Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv Res. 2018; 18: 545.