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La IA no nos salvará... aún: Reimaginar el potencial de la IA para priorizar a las personas
Lekshmi Santhosh1, Leah J Witt1,2
1Division of Pulmonary, Critical Care, Allergy & Sleep Medicine, University of California, San Francisco, San Francisco, CA.
ATS scholar
|February 21, 2026
Resumen
La inteligencia artificial (IA) se muestra prometedora para las tareas de atención médica, pero no logra abordar problemas sistémicos en la educación médica y el sistema de salud de los Estados Unidos. La verdadera transformación requiere que la IA se centre en los desafíos y la equidad centrados en las personas.
Área de la Ciencia:
- Innovación en el cuidado de la salud.
- Tecnología de la educación médica tecnología de la educación médica.
- Aplicaciones de inteligencia artificial aplicaciones de inteligencia artificial.
Sus antecedentes:
- La inteligencia artificial (IA) se promueve con frecuencia como una solución para los desafíos de la atención médica, como la carga administrativa y la precisión del diagnóstico.
- Sin embargo, las aplicaciones actuales de IA a menudo pasan por alto problemas sistémicos más profundos dentro del sistema de salud de los Estados Unidos y la educación médica.
Objetivo del estudio:
- Evaluar críticamente las limitaciones de la IA para abordar cuestiones fundamentales en el cuidado de la salud y la educación médica.
- Identificar áreas clave en las que la IA se queda corta, particularmente en los aspectos centrados en las personas.
Principales métodos:
- Análisis cualitativo de las tendencias actuales de IA en el cuidado de la salud y la educación médica.
- Identificación de cinco áreas críticas donde el impacto de la IA es insuficiente o potencialmente perjudicial.
Principales resultados:
- La IA lucha con las disfunciones sistémicas de la atención médica, la equidad en la salud, la carga de trabajo del médico, la educación médica y las crisis de la fuerza laboral.
- La trayectoria actual de la IA puede empeorar los problemas existentes en lugar de resolverlos.
Conclusiones:
- La IA por sí sola no puede arreglar el sistema de salud de los Estados Unidos o la educación médica sin abordar fallas sistémicas.
- El cambio transformacional requiere que la IA se desarrolle con expertos clínicos y pacientes, centrándose en la equidad y la sostenibilidad de la fuerza laboral.
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