Inteligencia artificial y enfoques diagnósticos multimodales en enfermedad cardiovascular

Objective. Evaluate the impact and clinical applicability of artificial intelligence (AI) models in cardiovascular diagnosis, assessing their potential to improve diagnostic accuracy, operational efficiency, and reliability compared with conventional methods. Materials and Methods. A critical review of the recent literature was conducted, encompassing retrospective studies, multicenter trials, and external validations that employed machine learning and deep learning algorithms applied to imaging modalities, electrocardiographic and phonocardiographic signals, as well as clinical and proteomic biomarkers. Results. Evidence indicates that in cardiac imaging, automated segmentation and ventricular dysfunction detection achieved accuracy metrics exceeding 90%, suggesting readiness for clinical integration. In cardiac signals, deep learning models demonstrated area under the ROC curve values of approximately 0.99 for predicting atrial fibrillation and ischemic heart disease, further supported by explainability techniques. Regarding biomarkers, ensemble models achieved diagnostic accuracies above 95%, and the integration of proteomic and clinical data substantially enhanced predictive performance. Nonetheless, decreased performance in external validations, limited generalizability to heterogeneous populations, and clinicians’ reluctance due to insufficient explainability remain major barriers. Conclusion. Artificial intelligence in cardiovascular diagnostics holds transformative potential by improving accuracy, reducing interobserver variability, and expanding access in resource-limited settings. However, its consolidation into routine practice requires robust multicenter validations, seamless interoperability with clinical workflows, and strengthened explainability, prerequisites for incorporation into clinical guidelines and precision medicine strategies.