Artificial Intelligence in Valvular Heart Disease: Current Applications and Future Perspectives
Inteligencia artificial en la enfermedad valvular cardíaca: aplicaciones actuales y perspectivas futuras
Debabrata Dash, Umanshi Dash, Abdul Rauoof Malik, Naeem Hasanfatta, Sreenivas Reddy, Sugandhna Reno Malan
Abstract
Valvular heart disease (VHD) remains a major contributor to global cardiovascular morbidity and mortality, with diagnosis and management relying on complex multimodality imaging and expert interpretation. This narrative review aims to evaluate the current applications and future potential of artificial intelligence (AI) in VHD. A comprehensive literature search was conducted using major databases, including PubMed, Scopus, and Web of Science, focusing on studies published in English that examined AI applications in VHD diagnosis, procedural planning, intraprocedural guidance, and prognostic assessment. Relevant original studies, clinical trials, and review articles were included based on their methodological quality and clinical relevance. Current evidence indicates that AI enhances early detection through accessible modalities such as electrocardiography and chest radiography, while significantly improving the accuracy and reproducibility of echocardiographic assessment. AI also facilitates precise preprocedural planning for transcatheter interventions and offers real-time support during procedures through multimodal image integration. In addition, AI-driven models enable robust risk stratification and prediction of clinical outcomes. Emerging innovations, including in silico trials and robotic-assisted interventions, further highlight AI’s transformative potential. Despite these advances, challenges related to data quality, bias, interpretability, and regulatory oversight remain. Continued validation and integration are essential to realize AI-driven precision medicine in VHD.
Keywords
Resumen
La enfermedad valvular cardiaca (EVC) sigue siendo una de las principales causas de morbilidad y mortalidad cardiovascular a nivel mundial, y su diagnóstico y tratamiento dependen de técnicas de imagen multimodal complejas e interpretación experta. Esta revisión narrativa tiene como objetivo evaluar las aplicaciones actuales y el potencial futuro de la inteligencia artificial (IA) en la EVC. Se realizó una búsqueda bibliográfica exhaustiva en las principales bases de datos, como PubMed, Scopus y Web of Science, centrándose en estudios publicados en inglés que examinaran las aplicaciones de la IA en el diagnóstico, la planificación de procedimientos, la guía intraoperatoria y la evaluación pronóstica de la EVC. Se incluyeron estudios originales, ensayos clínicos y artículos de revisión relevantes en función de su calidad metodológica y relevancia clínica. La evidencia actual indica que la IA mejora la detección temprana mediante modalidades accesibles como la electrocardiografía y la radiografía de tórax, a la vez que mejora significativamente la precisión y la reproducibilidad de la evaluación ecocardiográfica. La IA también facilita la planificación preoperatoria precisa para las intervenciones transcatéter y ofrece asistencia en tiempo real durante los procedimientos mediante la integración de imágenes multimodales. Además, los modelos basados en IA permiten una estratificación de riesgo sólida y la predicción de resultados clínicos. Las innovaciones emergentes, como los ensayos in silico y las intervenciones robóticas, ponen de manifiesto el potencial transformador de la IA. A pesar de estos avances, persisten desafíos relacionados con la calidad de los datos, los sesgos, la interpretabilidad y la supervisión regulatoria. La validación e integración continuas son esenciales para lograr una medicina de precisión basada en la IA en las enfermedades hemolíticas virales.
Palabras clave
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Submitted date:
01/08/2026
Reviewed date:
03/18/2026
Accepted date:
04/10/2026
Publication date:
04/13/2026
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