How prepared are future doctors for artificial intelligence?A mixed study of perceived importance, reliability, and classroom obstacles
¿Qué tan preparados están los futuros médicos para la inteligencia artificial?Un estudio mixto sobre importancia percibida, confiabilidad y obstáculos en el aula.
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Abstract
Introduction: Artificial intelligence (AI) is emerging as a transformative tool in medical education, with applications including clinical simulation, intelligent tutors and natural language processing. Although its potential is recognized, training gaps persist in the Colombian medical curriculum, especially at preclinical levels.
Objective: To characterize the perceptions, attitudes, familiarity levels and patterns of use of AI tools in medical students, and to identify associations between academic, technological and attitudinal variables to guide future curricular decisions.
Materials and methods: A mixed, predominantly quantitative, descriptive-analytical study was carried out in a higher education institution in Manizales (Colombia) during the 2024-2 semester. A total of 267 students of 1st, 2nd and 7th semesters of Medicine participated. Digital surveys were administered at the beginning and end of the semester. Descriptive analysis, χ² tests, Kruskal-Wallis and binary logistic regression were performed. Qualitative analysis was developed using natural language processing techniques and manual validation.
Main outcome: Level of AI use, perceived usefulness, conceptual familiarity, digital skills and comfort in its application.
Results: 87 % of first semester students and 100 % of seventh semester students reported use of AI. Familiarity with AI was significantly associated with digital skill level (χ² = 51.1, p < 0.001). No differences were found by gender. 83.1 % expressed interest in integrating AI into their training. Barriers such as lack of teaching guidance and perceived complexity were identified.
Conclusion: The findings show a high adoption and early acceptance of AI among medical students, with formative differences between semesters. A progressive curricular integration is recommended, differentiated by academic level and oriented under principles of reliable AI.
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