Early Exposure to Artificial Intelligence in Medical Education: A Knowledge, Attitude and Practice Study among Preclinical Medical Students from Mangaluru, India

Abstract

Introduction: Artificial Intelligence (AI) is the study of machines that learn from collected data and use human-like reasoning. AI applications are already used in healthcare, and there is a need to understand the need for AI training in the medical curriculum. Aim: To assess the Knowledge, Attitudes, and Practices (KAP) related to AI among first- and second-year MBBS students. Materials and Methods: This cross-sectional study was conducted at AJ Institute of Medical Sciences and Research Centre, Mangaluru, Karnataka, India from April to May 2026, among first- and second-year MBBS students via an online questionnaire; the sample size was 327. Based on existing KAP studies, the questionnaire collected data on AI knowledge (e.g., AI/ML concepts and applications), attitudes (e.g., beliefs about fostering AI knowledge despite its future application), and AI application. Categorical associations were analysed using Chisquare tests. Statistical significance was set at p-value <0.05. Results: Of 327 survey participants, 204 (62.39%) were firstyear medical students. Most reported at least basic knowledge of AI; however, few had prior training {26 (7.95%) attended AI workshops, and 41 (12.54%) received curriculum training}. Most students believed medical schools should teach AI concepts (269, 82.26%) and supported incorporating AI into the curriculum (193, 59.02%). ChatGPT was the most popular AI tool, used by 315 (96.33%) respondents, primarily for exam preparation and studying, with other uses less frequent. The key obstacles included insufficient knowledge/lack of proper training in AI (222, 67.89%) and concerns about data privacy and ethical issues (157,48.01%). Conclusion: Despite extensive exposure to AI (as most students used AI tools), students had limited knowledge or training in it. Students were optimistic about AI in healthcare and strongly supported AI education. These results suggest incorporating AI training into the medical curriculum to bridge knowledge gaps and meet learners’ needs.