Desarrollo Académico y Profesional en el Área de la Salud con Uso de IA
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Tribunal
Abstract
El estudio evalúa el impacto de la inteligencia artificial (IA) en la formación académica y el desarrollo profesional en salud, abarcando educación médica y práctica clínica entre 2020-2024. Su alcance consideró investigaciones indexadas en bases como Scopus y Web of Science. Metodológicamente aplicó el marco PICOT para definir la pregunta y criterios, y el protocolo PRISMA para la búsqueda, selección, elegibilidad e inclusión; se emplearon descriptores y operadores booleanos, criterios de inclusión/exclusión y registro sistemático de metadatos. Los resultados muestran una adopción creciente de herramientas de IA para predicción, diagnóstico, monitorización y apoyo docente; evidencian ventajas en eficiencia, personalización y detección temprana, junto a desafíos técnicos, éticos y de confianza. Se concluye que la IA tiene potencial transformador en salud y educación, pero requiere mayor validación, regulación y formación para su integración segura y equitativa.
The study evaluates the impact of artificial intelligence (AI) on academic training and professional development in health, covering medical education and clinical practice between 2020-2024. Its scope considered research indexed in databases such as Scopus and Web of Science. Methodologically, it applied the PICOT framework to define the question and criteria, and the PRISMA protocol for search, selection, eligibility, and inclusion; descriptors and Boolean operators, inclusion/exclusion criteria, and systematic metadata registration were used. The results show a growing adoption of AI tools for prediction, diagnosis, monitoring, and teaching support; they demonstrate advantages in efficiency, personalization, and early detection, along with technical, ethical, and trust challenges. It is concluded that AI has transformative potential in health and education, but requires further validation, regulation, and training for its safe and equitable integration.
The study evaluates the impact of artificial intelligence (AI) on academic training and professional development in health, covering medical education and clinical practice between 2020-2024. Its scope considered research indexed in databases such as Scopus and Web of Science. Methodologically, it applied the PICOT framework to define the question and criteria, and the PRISMA protocol for search, selection, eligibility, and inclusion; descriptors and Boolean operators, inclusion/exclusion criteria, and systematic metadata registration were used. The results show a growing adoption of AI tools for prediction, diagnosis, monitoring, and teaching support; they demonstrate advantages in efficiency, personalization, and early detection, along with technical, ethical, and trust challenges. It is concluded that AI has transformative potential in health and education, but requires further validation, regulation, and training for its safe and equitable integration.
Description
Vol. 5, No. 13