Aplicación de modelos de inteligencia artificial en la interpretación de radiografías de tórax para el diagnóstico de tuberculosis pulmonar
DOI:
https://doi.org/10.71112/5vy5w189Palabras clave:
Tuberculosis pulmonar;, Inteligencia artificial;, Deep learning;, Machine learning;, Revisión de alcance.Resumen
La tuberculosis pulmonar (TBP) continúa siendo una causa relevante de morbilidad y mortalidad a nivel mundial. La inteligencia artificial (IA), especialmente mediante técnicas de aprendizaje profundo, se ha posicionado como una herramienta prometedora para el diagnóstico automatizado mediante radiografías de tórax. El objetivo de esta revisión de alcance fue mapear y caracterizar la evidencia científica sobre modelos de IA aplicados al diagnóstico de TBP. Se realizó una búsqueda sistemática en seis bases de datos internacionales siguiendo la metodología del Instituto Joanna Briggs y las directrices PRISMA-ScR. Se incluyeron veinte estudios publicados entre 2020 y 2025, predominando arquitecturas CNN, híbridas con transformers y modelos de segmentación contextual. Los algoritmos alcanzaron exactitudes entre 96 % y 99.9 %. La IA mostró un desempeño superior al juicio humano en entornos controlados, aunque persisten desafíos en estandarización, validación clínica y representatividad geográfica.
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Derechos de autor 2026 Nelson Magdiel López Díaz, Edgardo Josué Ramos Rivas, Jacqueline Stefany Ramos Torres (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
