Publication: Predictores Individuales y Profesionales del Uso de Inteligencia Artificial en Medicina : Un Análisis Multivariado en América Latina
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Date
2026
Authors
Nouri Kandany, Vahid ; Fabián Vásquez, Lamec ; Valdez, Pascual ; Jiménez Arias, Juana ; Valdez Disla, Alexander
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Publisher
Universidad de Murcia, Servicio de Publicaciones
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DOI
https://doi.org/10.6018/edumed.685291
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info:eu-repo/semantics/article
Description
Abstract
Este estudio analiza los factores individuales y profesionales asociados al uso de la inteligencia artificial (IA) en la práctica médica en América Latina. Se realizó un estudio transversal y analítico mediante una encuesta estructurada aplicada a 1,041 médicos de 18 países, que incluyó variables sociodemográficas, formación académica, conocimientos estadísticos, familiaridad con la IA, actitudes, experiencia de uso y toma de decisiones en escenarios clínicos simulados. El análisis comprendió estadística descriptiva, pruebas bivariadas y regresión logística multivariada. La edad media de los participantes fue de 51.5 ± 13.5 años; el 77.5 % eran especialistas y el 50.5 % ejercía docencia universitaria. Aunque la mayoría manifestó haber oído hablar de IA, predominó un nivel básico de conocimiento (51.8 %), con una proporción reducida de formación avanzada (3.7 %). Se observó una marcada preferencia por el juicio clínico humano (86.0 %), junto con preocupaciones relacionadas con la falta de empatía (28.0 %) y la limitada personalización del cuidado (25.0 %).En el modelo multivariado final, la familiaridad con la inteligencia artificial se identificó como el predictor más fuerte del uso de IA en la práctica médica (OR = 4.59; IC 95 %: 3.10–6.81). Asimismo, la percepción de utilidad de la IA se asoció de manera significativa con una mayor probabilidad de adopción (OR = 2.49; IC 95 %: 1.07–5.81). En contraste, el escepticismo frente a la capacidad diagnóstica de la IA y el conocimiento técnico básico no mostraron asociaciones independientes significativas tras el ajuste del modelo. Una actitud favorable hacia la IA se relacionó significativamente con una mayor disposición a seguir sus recomendaciones en escenarios clínicos simulados (χ² = 75.2; p < 0.001). En conjunto, los resultados indican que la adopción de la inteligencia artificial en la práctica médica latinoamericana depende principalmente de la familiaridad práctica y del valor percibido, más que del dominio técnico avanzado. Estos hallazgos respaldan la necesidad de estrategias formativas y normativas orientadas a una integración crítica, responsable y contextualizada de la IA, preservando el papel central del juicio humano en la toma de decisiones médicas.
This study examines the individual and professional factors associated with the use ofartificial intelligence (AI) in medical practice across Latin America. A cross-sectional analyticalstudy was conducted using a structured survey administered to 1,041 physicians from 18 countries.Data collected included sociodemographic characteristics, academic background, statisticaltraining, AI familiarity, attitudes, prior use, and decision-making in simulated clinical scenarios. Analyses comprised descriptive statistics, bivariate tests, and multivariate logistic regression. Themean age of participants was 51.5 ± 13.5 years; 77.5% were specialists and 50.5% held universityteaching positions. Although AI awareness was widespread, most physicians reported basicknowledge (51.8%), with a small proportion indicating advanced training (3.7%). A strongpreference for human clinical judgment predominated (86.0%), accompanied by concerns regardinglack of empathy (28.0%) and limited personalization of care (25.0%). In the final multivariate model,AI familiarity emerged as the strongest predictor of use (OR = 4.59; 95% CI: 3.10–6.81). Perceivedusefulness of AI was also independently associated with a higher likelihood of adoption (OR = 2.49;95% CI: 1.07–5.81). In contrast, diagnostic skepticism and basic technical knowledge were notindependently associated with AI use after adjustment. A favorable attitude toward AI wassignificantly related to a greater willingness to follow AI recommendations in simulated clinicalscenarios (χ² = 75.2; p < 0.001). Overall, AI adoption in Latin American medical practice appears tobe driven primarily by practical familiarity and perceived value, rather than by advanced technicalexpertise. These findings support the need for educational and regulatory strategies that foster acritical, responsible, and context-sensitive integration of AI, while preserving the central role ofhuman judgment in medical decision-making.
This study examines the individual and professional factors associated with the use ofartificial intelligence (AI) in medical practice across Latin America. A cross-sectional analyticalstudy was conducted using a structured survey administered to 1,041 physicians from 18 countries.Data collected included sociodemographic characteristics, academic background, statisticaltraining, AI familiarity, attitudes, prior use, and decision-making in simulated clinical scenarios. Analyses comprised descriptive statistics, bivariate tests, and multivariate logistic regression. Themean age of participants was 51.5 ± 13.5 years; 77.5% were specialists and 50.5% held universityteaching positions. Although AI awareness was widespread, most physicians reported basicknowledge (51.8%), with a small proportion indicating advanced training (3.7%). A strongpreference for human clinical judgment predominated (86.0%), accompanied by concerns regardinglack of empathy (28.0%) and limited personalization of care (25.0%). In the final multivariate model,AI familiarity emerged as the strongest predictor of use (OR = 4.59; 95% CI: 3.10–6.81). Perceivedusefulness of AI was also independently associated with a higher likelihood of adoption (OR = 2.49;95% CI: 1.07–5.81). In contrast, diagnostic skepticism and basic technical knowledge were notindependently associated with AI use after adjustment. A favorable attitude toward AI wassignificantly related to a greater willingness to follow AI recommendations in simulated clinicalscenarios (χ² = 75.2; p < 0.001). Overall, AI adoption in Latin American medical practice appears tobe driven primarily by practical familiarity and perceived value, rather than by advanced technicalexpertise. These findings support the need for educational and regulatory strategies that foster acritical, responsible, and context-sensitive integration of AI, while preserving the central role ofhuman judgment in medical decision-making.
Citation
Nouri Kandany, V., Fabián Vásquez, L., Valdez, P., Jiménez Arias, J., & Valdez Disla, A. (2026). Predictores Individuales y Profesionales del Uso de Inteligencia Artificial en Medicina: Un Análisis Multivariado en América Latina. Revista Española De Educación Médica, 7(1). https://doi.org/10.6018/edumed.685291
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