La transformación de la práctica cardiológica por la inteligencia artificial: Desde el cribado clínico hasta la toma de decisiones personalizadas
Keywords:
Inteligencia Artificial, Cardiología, Aprendizaje Automático (Machine Learning), Medicina de Precisión, Sesgo Algorítmico, Sistemas de Apoyo a la Decisión ClínicaAbstract
Introducción: La práctica cardiológica contemporánea enfrenta limitaciones humanas inherentes, como la variabilidad interobservador y la inercia terapéutica, en un contexto de creciente demanda asistencial. La inteligencia artificial (IA) emerge como un paradigma disruptivo capaz de redefinir el continuo de atención, desde el cribado hasta la medicina de precisión. Esta revisión integrativa analiza el impacto clínico, pronóstico y ético de la IA en la cardiología moderna.
Métodos: Se realizó una revisión integrativa siguiendo el marco de Whittemore y Knafl. Se llevó a cabo una búsqueda sistemática en PubMed, Scopus y Google Scholar de estudios publicados entre 2019 y 2025. Se incluyeron investigaciones originales y revisiones sistemáticas sobre aprendizaje automático (Machine Learning) y aprendizaje profundo (Deep Learning) aplicados al diagnóstico, pronóstico y tratamiento cardiovascular.
Resultados: La síntesis de la evidencia revela que los algoritmos de IA en dispositivos ponibles (wearables) alcanzan una sensibilidad del 99.6 % para la detección de fibrilación auricular, facilitando el cribado masivo. En el ámbito pronóstico, los modelos de aprendizaje automático superan a los scores tradicionales (Framingham/SCORE), elevando el índice-C de 0.61 a 0.68 en la predicción de eventos adversos mayores (MACE). Asimismo, la automatización en imagenología (RMN/TC) y el fenotipado digital optimizan la precisión diagnóstica y la personalización terapéutica. Sin embargo, persisten barreras críticas para la implementación, destacando la opacidad de los algoritmos tipo «caja negra» y el riesgo de sesgos algorítmicos en poblaciones subrepresentadas, lo que amenaza la equidad en salud.
Conclusiones: La IA no reemplaza al cardiólogo, sino que instaura una era de «inteligencia aumentada» que potencia la precisión diagnóstica y la estratificación del riesgo. La transición hacia la práctica clínica rutinaria requiere superar desafíos de explicabilidad, validar modelos en poblaciones diversas para mitigar sesgos y garantizar la interoperabilidad de los sistemas.
Downloads
References
Ahmed MM, Okesanya OJ, Olaleke NO, Adigun OA, Adebayo UO, Oso TA, et al. Integrating digital health innovations to achieve universal health coverage: Promoting health outcomes and quality through global public health equity. Healthcare (Basel) [Internet]. 2025 May 5;13(9):1060. Available from: http://dx.doi.org/10.3390/healthcare13091060
Song J, Wang X, Wang B, Gao Y, Liu J, Zhang H, et al. Effectiveness of a clinical decision support system for hypertension management in primary care: study protocol for a pragmatic cluster-randomized controlled trial. Trials [Internet]. 2022 May 16;23(1):412. Available from: http://dx.doi.org/10.1186/s13063-022-06374-x
Antoun I, Abdelrazik A, Eldesouky M, Li X, Layton GR, Zakkar M, et al. Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations. Front Cardiovasc Med [Internet]. 2025 Jun 24;12:1596574. Available from: http://dx.doi.org/10.3389/fcvm.2025.1596574
Kolossváry M, Sereti I, Zanni MV, Fichtenbaum CJ, Aberg JA, Bloomfield GS, et al. Statin-dependent and -independent pathways are associated with major adverse cardiovascular events in people with HIV. J Clin Invest [Internet]. 2025 Nov 17;135(22). Available from: http://dx.doi.org/10.1172/jci196021
Tamarappoo BK, Lin A, Commandeur F, McElhinney PA, Cadet S, Goeller M, et al. Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study. Atherosclerosis [Internet]. 2021 Feb;318:76–82. Available from: http://dx.doi.org/10.1016/j.atherosclerosis.2020.11.008
Santala OE, Lipponen JA, Jäntti H, Rissanen TT, Tarvainen MP, Laitinen TP, et al. Continuous mHealth patch monitoring for the algorithm-based detection of atrial fibrillation: Feasibility and diagnostic accuracy study. JMIR Cardio [Internet]. 2022 Jun 21;6(1):e31230. Available from: http://dx.doi.org/10.2196/31230
Böttcher B, Beller E, Busse A, Cantré D, Yücel S, Öner A, et al. Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm. Int J Cardiovasc Imaging [Internet]. 2020 Nov;36(11):2239–47. Available from: http://dx.doi.org/10.1007/s10554-020-01935-0
Gautam A, Raghav P, Subramaniam V, Kumar S, Kumar S, Jain D, et al. Fully automated Agatston score calculation from electrocardiography-gated cardiac computed tomography using deep learning and multi-organ segmentation: A validation study. Angiology [Internet]. 2025 May;76(5):431–40. Available from: http://dx.doi.org/10.1177/00033197231225286
Hicks SA, Isaksen JL, Thambawita V, Ghouse J, Ahlberg G, Linneberg A, et al. Explaining deep neural networks for knowledge discovery in electrocardiogram analysis. Sci Rep [Internet]. 2021 May 26;11(1):10949. Available from: http://dx.doi.org/10.1038/s41598-021-90285-5
Zeineldin RA, Karar ME, Elshaer Z, Coburger J, Wirtz CR, Burgert O, et al. Explainable hybrid vision transformers and convolutional network for multimodal glioma segmentation in brain MRI. Sci Rep [Internet]. 2024 Feb 14;14(1):3713. Available from: http://dx.doi.org/10.1038/s41598-024-54186-7
Puyol-Antón E, Ruijsink B, Mariscal Harana J, Piechnik SK, Neubauer S, Petersen SE, et al. Fairness in cardiac magnetic resonance imaging: Assessing sex and racial bias in deep learning-based segmentation. Front Cardiovasc Med [Internet]. 2022 Apr 7;9(859310):859310. Available from: http://dx.doi.org/10.3389/fcvm.2022.859310
Vardas EP, Marketou M, Vardas PE. Medicine, healthcare and the AI act: gaps, challenges and future implications. Eur Heart J Digit Health [Internet]. 2025 Jul;6(4):833–9. Available from: http://dx.doi.org/10.1093/ehjdh/ztaf041
Chen E, Jiang J, Su R, Gao M, Zhu S, Zhou J, et al. A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation. Heart Rhythm [Internet]. 2020 May;17(5 Pt B):847–53. Available from: http://dx.doi.org/10.1016/j.hrthm.2020.01.034
Baek YS, Kwon S, You SC, Lee KN, Yu HT, Lee SR, et al. Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study. Front Cardiovasc Med [Internet]. 2023 Oct 11;10:1258167. Available from: http://dx.doi.org/10.3389/fcvm.2023.1258167
Khalifa A, Abdulaziz M, Parvez SS, Salem A, Panhwer HS, Saleem U, et al. The digital revolution in cardiac ischemia: Artificial intelligence (AI)-enhanced detection, diagnosis, and risk stratification. Cureus [Internet]. 2025 Oct;17(10):e95059. Available from: http://dx.doi.org/10.7759/cureus.95059
Giebel GD, Gissel C. Accuracy of mHealth devices for atrial fibrillation screening: Systematic Review. JMIR MHealth UHealth [Internet]. 2019 Jun 16;7(6):e13641. Available from: http://dx.doi.org/10.2196/13641
Hartikainen S, Lipponen JA, Hiltunen P, Rissanen TT, Kolk I, Tarvainen MP, et al. Effectiveness of the chest strap electrocardiogram to detect atrial fibrillation. Am J Cardiol [Internet]. 2019 May 15;123(10):1643–8. Available from: http://dx.doi.org/10.1016/j.amjcard.2019.02.028
Duffy G, Cheng PP, Yuan N, He B, Kwan AC, Shun-Shin MJ, et al. High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning. JAMA Cardiol [Internet]. 2022 Apr 1;7(4):386–95. Available from: http://dx.doi.org/10.1001/jamacardio.2021.6059
Maturi B, Dulal S, Sayana SB, Ibrahim A, Ramakrishna M, Chinta V, et al. Revolutionizing cardiology: The role of artificial intelligence in echocardiography. J Clin Med [Internet]. 2025 Jan 19;14(2):625. Available from: http://dx.doi.org/10.3390/jcm14020625
Ghorbani A, Ouyang D, Abid A, He B, Chen JH, Harrington RA, et al. Deep learning interpretation of echocardiograms. NPJ Digit Med [Internet]. 2020 Jan 24;3(1):10. Available from: http://dx.doi.org/10.1038/s41746-019-0216-8
Wang J, Zhang N, Wang S, Liang W, Zhao H, Xia W, et al. AI approach to biventricular function assessment in cine-MRI: an ultra-small training dataset and multivendor study. Phys Med Biol [Internet]. 2023 Dec 13;68(24):245025. Available from: http://dx.doi.org/10.1088/1361-6560/ad0903
Gautam A, Raghav P, Subramanya V, Kumar S, Kumar S, Jain D, et al. Fully Automated Agatston score calculation from ECG gated Cardiac CT using Deep learning and Multiorgan Segmentation: A Validation study [Internet]. Research Square. 2022. Available from: http://dx.doi.org/10.21203/rs.3.rs-2347144/v1
Tian D, Yu N, Mao T, Li Y, Liu R, Xu Y, et al. Prediction of long-term major adverse cardiac events after myocardial infarction: value of combination of inflammatory biomarkers and GRACE score. Front Cardiovasc Med [Internet]. 2025 Jul 10;12(1591578):1591578. Available from: http://dx.doi.org/10.3389/fcvm.2025.1591578
Shinohara H, Kodera S, Nagae Y, Hiruma T, Kobayashi A, Sato M, et al. The potential of the transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients with ischemic heart disease. PLoS One [Internet]. 2024 Jun 18;19(6):e0304423. Available from: http://dx.doi.org/10.1371/journal.pone.0304423
Moltó-Balado P, Reverté-Villarroya S, Alonso-Barberán V, Monclús-Arasa C, Balado-Albiol MT, Clua-Queralt J, et al. Machine learning approaches to predict Major Adverse Cardiovascular Events in atrial fibrillation. Technologies (Basel) [Internet]. 2024 Jan 23;12(2):13. Available from: http://dx.doi.org/10.3390/technologies12020013
Oo MM, Gao C, Cole C, Hummel Y, Guignard-Duff M, Jefferson E, et al. Artificial intelligence-assisted automated heart failure detection and classification from electronic health records. ESC Heart Fail [Internet]. 2024 Oct;11(5):2769–77. Available from: http://dx.doi.org/10.1002/ehf2.14828
Shao Y, Zhang S, Raman VK, Patel SS, Cheng Y, Parulkar A, et al. Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record. ESC Heart Fail [Internet]. 2024 Oct;11(5):3155–66. Available from: http://dx.doi.org/10.1002/ehf2.14787
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 [Internet]. 2016 Nov;9(6):629–40. Available from: http://dx.doi.org/10.1161/circoutcomes.116.003039
Banerjee A, Chen S, Fatemifar G, Zeina M, Lumbers RT, Mielke J, et al. Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility. BMC Med [Internet]. 2021 Apr 6;19(1):85. Available from: http://dx.doi.org/10.1186/s12916-021-01940-7
Weller J, Gutton J, Hocquet G, Pellet L, Aroulanda MJ, Bruandet A, et al. Prediction of 90 day mortality in elderly patients with acute HF from e-health records using artificial intelligence. ESC Heart Fail [Internet]. 2025 Jun;12(3):2200–9. Available from: http://dx.doi.org/10.1002/ehf2.15244
Kwon JM, Kim KH, Jeon KH, Lee SE, Lee HY, Cho HJ, et al. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure. PLoS One [Internet]. 2019 Jul 8;14(7):e0219302. Available from: http://dx.doi.org/10.1371/journal.pone.0219302
Kasem Ali Sliman R, Eitan A, Zisman K, Avidan Y, Aker A, Sliman H. Three-cusp view orientation in TAVI: Evaluating LOA versus non-LOA impact on outcomes with contemporary valve prostheses. Catheter Cardiovasc Interv [Internet]. 2025 Oct;106(4):2567–78. Available from: http://dx.doi.org/10.1002/ccd.70087
Butter C, Kaneko H, Tambor G, Hara M, Neuss M, Hoelschermann F. Clinical utility of intraprocedural three-dimensional integrated image guided transcatheter aortic valve implantation using novel automated computed tomography software: A single-center preliminary experience. Catheter Cardiovasc Interv [Internet]. 2019 Mar 1;93(4):722–8. Available from: http://dx.doi.org/10.1002/ccd.27920
Felbel D, Buck C, Riedel N, Paukovitsch M, Stephan T, Krohn-Grimberghe M, et al. Combined computed coronary tomography angiography and transcatheter aortic valve implantation (TAVI) planning computed tomography reliably detects relevant coronary artery disease pre-TAVI. J Clin Med [Internet]. 2024 Aug 19;13(16):4885. Available from: http://dx.doi.org/10.3390/jcm13164885
Panuccio G, Abdelwahed YS, Carabetta N, Salerno N, Leistner DM, Landmesser U, et al. Clinical and procedural outcomes of IVUS-guided vs. Angiography-guided CTO-PCI: A systematic review and meta-analysis. J Clin Med [Internet]. 2023 Jul 27;12(15):4947. Available from: http://dx.doi.org/10.3390/jcm12154947
Hoque A, Ahmed I, Nahar N, Saria SJ, Islam S, MD Mehedi Hasan, et al. Ai-assisted imaging and navigation in Minimally invasive cardiac interventions - a systematic review. Insights-Journal of Health and Rehabilitation [Internet]. 2025 Apr 21;3(2 (Health & Allied)):538–44. Available from: http://dx.doi.org/10.71000/ky41as49
Hopkisson O, Ibrahem A, Swinn T, Dastidar A, Satti Z. Contemporary review of cardiovascular computed tomography in coronary and structural heart interventions [Internet]. Preprints. 2025. Available from: http://dx.doi.org/10.20944/preprints202502.1726.v1
Ashiq K, Ashiq S, Mustafa N. Pharmacogenomics and the concept of personalized medicine for the management of hypertension. Pak Hear J [Internet]. 2023 Jun 29;56(2):188–90. Available from: http://dx.doi.org/10.47144/phj.v56i2.2553
Afrose N, Chakraborty R, Hazra A, Bhowmick P, Bhowmick M. AI-driven drug discovery and development. In: Future of AI in Biomedicine and Biotechnology [Internet]. IGI Global; 2024. p. 259–77. Available from: http://dx.doi.org/10.4018/979-8-3693-3629-8.ch013
Iftikhar H. AI-driven pharmacovigilance and molecular profiling of fluoroquinolone-associated cardiotoxicity in the UAE: A geospatial and machine learning analysis with structural modification strategies (2018-2023) [Internet]. medRxiv. medRxiv; 2025. Available from: http://dx.doi.org/10.1101/2025.05.10.25327319
Szymańska K, Szmyt K, Krasnoborska J, Samojedny S, Superson M, Walczak K, et al. Revolutionizing cardiovascular treatments with the use of AI: Current status and future prospects. Qual Sport [Internet]. 2024 Jul 28;19:53072. Available from: http://dx.doi.org/10.12775/qs.2024.19.53072
Chandan TR, Patil CD, Kundgir VB, Chaudhari K, Bachhav RL, Bhamare MS, et al. Pharmacogenomics and personalized medicine: A revolution in drug therapy. Res J Pharmacol Pharmacodyn [Internet]. 2025 Oct 11;311–8. Available from: http://dx.doi.org/10.52711/2321-5836.2025.00048
Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ [Internet]. 2005 Apr 2;330(7494):765. Available from: http://dx.doi.org/10.1136/bmj.38398.500764.8f
Cai J, Li P, Li W, Zhu T. Outcomes of clinical decision support systems in real-world perioperative care: a systematic review and meta-analysis. Int J Surg [Internet]. 2024 Dec 1;110(12):8057–72. Available from: http://dx.doi.org/10.1097/js9.0000000000001821
Bozyel S, Şimşek E, Koçyiğit Burunkaya D, Güler A, Korkmaz Y, Şeker M, et al. Artificial intelligence-based clinical decision support systems in cardiovascular diseases. Anatol J Cardiol [Internet]. 2024 Jan 7;74–86. Available from: http://dx.doi.org/10.14744/anatoljcardiol.2023.3685
Giuste F, Shi W, Zhu Y, Naren T, Isgut M, Sha Y, et al. Explainable Artificial Intelligence methods in combating pandemics: A systematic review. IEEE Rev Biomed Eng [Internet]. 2023 Jan 5;16:5–21. Available from: http://dx.doi.org/10.1109/rbme.2022.3185953
Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: A review of machine learning interpretability methods. Entropy (Basel) [Internet]. 2020 Dec 25;23(1):18. Available from: http://dx.doi.org/10.3390/e23010018
Aasem M, Javed Iqbal M. Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning. Front Big Data [Internet]. 2024 May 2;7:1366415. Available from: http://dx.doi.org/10.3389/fdata.2024.1366415
Petch J, Di S, Nelson W. Opening the black box: The promise and limitations of explainable machine learning in cardiology. Can J Cardiol [Internet]. 2022 Feb;38(2):204–13. Available from: http://dx.doi.org/10.1016/j.cjca.2021.09.004
Jones RK. Algorithmic bias and fairness in biomedical and health research. In: Advances in Computational Intelligence and Robotics [Internet]. IGI Global Scientific Publishing; 2025. p. 287–324. Available from: http://dx.doi.org/10.4018/979-8-3373-4252-8.ch008
Oyeniran OC, Adewusi AO, Adeleke AG, Akwawa LA, Azubuko CF. Ethical AI: Addressing bias in machine learning models and software applications. Comput sci IT res j [Internet]. 2022 Dec 30;3(3):115–26. Available from: http://dx.doi.org/10.51594/csitrj.v3i3.1559
Bhimavarapu U. Bias in AI-driven diabetes prediction models: Challenges, impacts, and mitigation strategies. In: Advances in Computational Intelligence and Robotics [Internet]. IGI Global; 2025. p. 195–214. Available from: http://dx.doi.org/10.4018/979-8-3693-9735-0.ch008
Saleiro P, Kuester B, Hinkson L, London J, Stevens A, Anisfeld A, et al. Aequitas: A bias and fairness audit toolkit [Internet]. arXiv [cs.LG]. 2018. Available from: http://dx.doi.org/10.48550/arxiv.1811.05577
Dakshit S, Dakshit S, Khargonkar N, Prabhakaran B. Bias analysis in healthcare time series (BAHT) decision support systems from meta data. J Healthc Inform Res [Internet]. 2023 Jun;7(2):225–53. Available from: http://dx.doi.org/10.1007/s41666-023-00133-6
Al-Nafjan A, Aljuhani A, Alshebel A, Alharbi A, Alshehri A. Artificial intelligence in predictive healthcare: A systematic review. J Clin Med [Internet]. 2025 Sep 24;14(19):6752. Available from: http://dx.doi.org/10.3390/jcm14196752
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Héctor Adrián Franco (Autor/a); María Paz Fleitas Acosta (Traductor/a); Luciano Javier Zarate Mercado, Wyder Dario Pacce Mallorquín, Ruth Estefania Mallorquin Ortellado, Jonathan Salinas, Vinicius da Silva Souza, Lígia Maria Oliveira de Souza (Autor/a)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Los artículos publicados en la Revista UniNorte de Medicina y Ciencias de la Salud se distribuyen bajo una licencia Creative Commons Reconocimiento 4.0 Internacional (CC BY 4.0).
Esta licencia permite a cualquier usuario copiar, distribuir, adaptar, transformar y construir a partir del material para cualquier propósito, incluso comercial, siempre que se otorgue el crédito adecuado a los autores y a la fuente original de publicación.
Los autores conservan los derechos de autor y otorgan a la revista el derecho de primera publicación.
La reutilización del contenido deberá reconocer la autoría, citar la fuente original e indicar si se han realizado modificaciones.
