Implementación y Evaluación de Herramientas de Inteligencia Artificial en Diagnósticos Médicos: Un Estudio de Casos en Medicina General

Abstract

Este estudio se centra en la implementación y evaluación de herramientas de inteligencia artificial (IA) en diagnósticos médicos, específicamente en el campo de la medicina general. El objetivo principal fue analizar la precisión diagnóstica y la eficiencia en el tiempo de diagnóstico mediante la comparación entre métodos tradicionales y aquellos asistidos por IA. Se utilizó un enfoque metodológico mixto, incluyendo un análisis cuantitativo y cualitativo. Para el análisis cuantitativo, se seleccionaron 50 casos clínicos donde se aplicaron herramientas de IA para comparar los resultados con diagnósticos convencionales. En el análisis cualitativo, se realizaron entrevistas a médicos sobre su experiencia y aceptación del uso de IA en su práctica diaria. Los resultados muestran que la IA aumenta la precisión diagnóstica en un 7% en promedio en comparación con los métodos tradicionales, y reduce el tiempo promedio de diagnóstico en 10 minutos. Sin embargo, se identificaron barreras en la adopción de estas herramientas, como la falta de capacitación y la resistencia al cambio por parte de algunos profesionales. Se concluye que la implementación de herramientas de IA en el diagnóstico médico puede mejorar tanto la precisión como la eficiencia, aunque es necesario superar ciertos desafíos relacionados con la formación del personal y la integración de la tecnología en los sistemas de salud.

The integration of artificial intelligence (AI) in medical diagnostics has emerged as a transformative force in healthcare, promising increased accuracy and efficiency in the diagnostic process. This study investigates the impact of AI on diagnostic precision in general medicine by comparing diagnostic outcomes between AI-assisted and traditional diagnostic methods. Using a mixed-methods approach, data were collected from 50 medical practitioners who utilized AI tools and 50 who relied on conventional diagnostic techniques. The research employed quantitative measures to assess diagnostic accuracy and time efficiency, along with qualitative interviews to gauge practitioner satisfaction and challenges. Results indicated a significant improvement in diagnostic accuracy, with AI-assisted diagnostics achieving a mean accuracy rate of 92%, compared to 85% for traditional methods. Additionally, the AI group reported a reduction in average diagnostic time by 10 minutes. Despite these benefits, challenges such as integration difficulties and user resistance were noted. The study concludes that AI enhances diagnostic precision and efficiency but highlights the need for continued efforts to address implementation barriers. These findings suggest that broader adoption of AI tools in medical practice could lead to more accurate and timely diagnoses, improving patient outcomes.The integration of artificial intelligence (AI) in medical diagnostics has emerged as a transformative force in healthcare, promising increased accuracy and efficiency in the diagnostic process. This study investigates the impact of AI on diagnostic precision in general medicine by comparing diagnostic outcomes between AI-assisted and traditional diagnostic methods. Using a mixed-methods approach, data were collected from 50 medical practitioners who utilized AI tools and 50 who relied on conventional diagnostic techniques. The research employed quantitative measures to assess diagnostic accuracy and time efficiency, along with qualitative interviews to gauge practitioner satisfaction and challenges. Results indicated a significant improvement in diagnostic accuracy, with AI-assisted diagnostics achieving a mean accuracy rate of 92%, compared to 85% for traditional methods. Additionally, the AI group reported a reduction in average diagnostic time by 10 minutes. Despite these benefits, challenges such as integration difficulties and user resistance were noted. The study concludes that AI enhances diagnostic precision and efficiency but highlights the need for continued efforts to address implementation barriers. These findings suggest that broader adoption of AI tools in medical practice could lead to more accurate and timely diagnoses, improving patient outcomes