Inteligencia artificial y diagnóstico médico
Artículo de Revisión
DOI:
https://doi.org/10.69825/cienec.v8i34.381Abstract
La inteligencia artificial (IA) ha emergido como una herramienta innovadora en el diagnóstico médico, permitiendo el análisis automatizado de grandes volúmenes de datos clínicos mediante algoritmos avanzados de aprendizaje automático y aprendizaje profundo. El objetivo de este estudio fue analizar la evidencia científica disponible sobre la aplicación de la IA en el diagnóstico médico, evaluando su rendimiento, utilidad clínica y limitaciones. Se realizó una revisión sistemática de la literatura siguiendo las directrices PRISMA 2020, mediante la búsqueda en bases de datos como PubMed, Scopus, Web of Science e IEEE Xplore, incluyendo estudios publicados entre 2015 y 2025. Se seleccionaron 15 estudios originales que cumplieron con los criterios de inclusión. Los resultados evidencian que los sistemas basados en IA, particularmente las redes neuronales convolucionales, presentan altos niveles de sensibilidad, especificidad y área bajo la curva, especialmente en el análisis de imágenes médicas en áreas como oftalmología, radiología y oncología. Asimismo, se identificaron aplicaciones relevantes en el análisis de señales biomédicas y predicción de riesgo clínico. No obstante, se observaron limitaciones relacionadas con la heterogeneidad de los datos, la falta de validación externa y la interpretabilidad de los modelos. En conclusión, la inteligencia artificial representa una herramienta prometedora para mejorar la precisión diagnóstica y optimizar la toma de decisiones clínicas. Sin embargo, su implementación requiere validación en entornos reales, desarrollo de marcos regulatorios y consideración de aspectos éticos para garantizar su uso seguro y equitativo en la práctica médica.
Artificial intelligence; Medical diagnosis; Deep learning; Machine learning; Diagnostic accuracy.
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