Transformación ciberorgánica: un enfoque crítico de las aplicaciones de inteligencia artificial en los servicios de salud
DOI:
https://doi.org/10.71164/socialmedicine.v19i3.2026.2069Palabras clave:
Health Informatics, Healthcare Predictive Analytics, Artificial Intelligence (AI)Resumen
Este estudio examina el papel creciente de la inteligencia artificial (IA), el aprendizaje automático (ML, machine learning) y las técnicas de aprendizaje profundo (DL, deeplearning) en la atención a la salud, a través de un análisis bibliométrico integral y una revisión de contenidos en la red basada en VOSviewer. Una evaluación de 4,637 artículos publicados entre 1992 y 2025 muestra que Estados Unidos, India, Reino Unido y China destacan tanto en volumen de publicaciones como en tasas de colaboración internacional. A nivel institucional, centros académicos líderes como la Universidad de Harvard (y sus Asociados Médicos) y la Universidad de Londres han desempeñado un papel pionero en el avance del campo. El análisis en redes por palabras clave destaca la creciente importancia de términos como inteligencia artificial, aprendizaje automático y aprendizaje profundo, junto con temas emergentes como sistemas de apoyo en la toma de decisiones clínicas, COVID-19 y grandes modelos de lenguaje (por ejemplo, ChatGPT), reflejando la creciente diversidad de la literatura. Los hallazgos bibliométricos destacan que las aplicaciones de IA para la atención a la salud cubren una amplia gama de dominios, incluida el procesamiento de imágenes clínicas, la supervisión remota de pacientes, la medicina personalizada y el procesamiento del lenguaje natural (NLP). El uso creciente de modelos de aprendizaje profundo en sistemas de apoyo para la toma de decisiones clínicas ilustra la creciente demanda de soluciones de salud digital en la era posterior a la COVID-19. Temas clave como la IA explicable (XAI), la privacidad de datos, consideraciones éticas y regulaciones legales se subrayan como dimensiones críticas para garantizar un progreso sostenible en el campo. La literatura también señala la importancia de conjuntos de datos más grandes y multicéntricos, colaboraciones internacionales más fuertes y la promoción de la alfabetización en IA como pasos esenciales hacia los avances futuros.
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
