La transformación de la práctica cardiológica por la inteligencia artificial: Desde el cribado clínico hasta la toma de decisiones personalizadas
DOI:
https://doi.org/10.5281/zenodo.17887328Palabras clave:
Inteligencia Artificial, Cardiología, Aprendizaje Automático (Machine Learning), Medicina de Precisión, Sesgo Algorítmico, Sistemas de Apoyo a la Decisión ClínicaResumen
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.
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Derechos de autor 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)
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