Inteligencia Artificial aplicada a los cuidados (I): origen y conceptos generales

• Reyes Grangel ^[1] ; María Victoria Ibáñez Gual ^[1] ; Amelia Simó Vidal ^[1] ; Juan Carlos Amengual Argudo ^[1] ; Óscar Belmonte Fernández ^[1]
  1. [1] Universitat Jaume I

     Universitat Jaume I

     Castellón, España

     
• Localización: Enfermería clínica, ISSN 1130-8621, Vol. 36, Nº. 3, 2026
• Idioma: español
• DOI: 10.1016/j.enfcli.2026.502486
• Títulos paralelos:
  • Artificial Intelligence applied to care (I): origin and general concepts
• Enlaces
  • Texto completo
• Resumen
  • español

    Desde la aparición de ChatGPT en noviembre de 2022, la Inteligencia Artificial (IA) ha ganado una notable popularidad, extendiéndose a múltiples ámbitos de la vida diaria y profesional. Sin embargo, la IA abarca mucho más que la inteligencia generativa, incluyendo técnicas que permiten analizar datos y apoyar la toma de decisiones, especialmente en contextos como el sanitario. Este auge hace necesario un esfuerzo de transparencia y explicabilidad de los modelos predictivos y de las técnicas utilizadas, con el fin de generar confianza entre los profesionales sanitarios que deberán aplicarlas.

    En este artículo se ofrece un resumen de las principales técnicas predictivas de IA aplicables al proceso asistencial en enfermería. Se describen métodos centrados en los datos (aprendizaje automático, redes neuronales profundas e IA generativa), así como técnicas centradas en análisis de procesos (monitoreo predictivo de procesos). Cada técnica se ilustra mediante ejemplos prácticos del ámbito sanitario, con el objetivo de facilitar su comprensión. Finalmente, se presentan las principales limitaciones de estas técnicas y sus perspectivas de futuro.

  • English

    Since the emergence of ChatGPT in November 2022, Artificial Intelligence has gained significant popularity, extending to multiple areas of daily and professional life. However, Artificial Intelligence encompasses much more than Generative Intelligence, including techniques that allow data analysis and support decision-making, especially in contexts such as healthcare. This boom necessitates efforts to ensure transparency and explainability of the predictive models and techniques used, to build trust among the healthcare professionals who must apply them.

    This article provides a summary of the main predictive Artificial Intelligence techniques applicable to the nursing care process. Data-centric methods are described (Machine Learning, Deep Neural Networks and Generative Artificial Intelligence), as well as process-centric techniques (Predictive Process Monitoring). Each technique is illustrated with practical examples from the healthcare field, with the aim of facilitating their understanding by healthcare professionals. Finally, the main limitations of these techniques and their prospects are presented.

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