Perceptions of Artificial Intelligence in Medical Education: A Cross-Sectional Study Among Students and Faculty at HBS Medical and Dental College, Islamabad

Abstract

As artificial intelligence (AI) continues to transform healthcare, its integration into medical education is increasingly critical. However, many institutions lack formal AI curricula, leaving students and faculty underprepared for the digital demands of clinical practice. Objectives: To assess awareness, familiarity, perceived benefits, and concerns regarding AI among medical students and faculty, and to explore training preferences and barriers to AI integration in academic settings. Methods: A descriptive cross-sectional survey was conducted at a HBS Medical and Dental College, with a total of 100 participants (76 students and 24 faculty). A questionnaire assessed demographic characteristics, AI familiarity, perceived benefits and concerns, and interest in formal training. Chi-square tests and logistic regression were used to analyse group differences and predictors of training interest. Results: Most participants (60%) were under 25 years old, and 76% were students. While 68% had heard of AI, only 43% reported basic familiarity. Interest in AI training was high (87%). Commonly cited benefits included faster knowledge access and personalized learning, while concerns focused on ethical issues and misinformation. A significant association was found between academic role and perceived lack of training (p=0.041). Logistic regression showed a non-significant trend linking prior AI exposure with interest in training (p=0.125). Conclusions: It was concluded that there is strong enthusiasm for AI in medical education among both students and faculty. However, limited familiarity and perceived barriers highlight the need for structured training and targeted curriculum reforms to build digital competence in future healthcare professionals.

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