Needs Assessment for an Artificial Intelligence Elective in Physiology among Medical Students and Faculty: A Cross-sectional Study using Kern’s Framework

Background and Aim: Artificial intelligence (AI) is rapidly transforming medical education by enhancing decision-making, personalizing learning, and improving patient outcomes. However, the integration of AI into medical curricula remains limited, with gaps in student and faculty preparedness. This study aimed to assess the awareness and perceptions of AI among undergraduate and postgraduate medical students, as well as faculty members, to develop a 2-week elective course on AI in Physiology tailored to their needs. Materials and Methods: A cross-sectional needs assessment study was conducted at UCMS and GTB Hospital, Delhi. A structured questionnaire was administered to 93 undergraduate students, 93 postgraduate students, and 93 faculty members (total n = 279) to evaluate knowledge, perceptions, and self-assessed competencies related to AI. The elective course was developed using Kern’s six-step framework. Results: The study revealed that 84.95% of undergraduate students, 86.02% of postgraduate students, and 78.49% of faculty members demonstrated good knowledge of AI. However, 60.57% of respondents felt that their medical education did not adequately prepare them to work alongside AI technologies. Furthermore, 74.55% agreed that AI training should be incorporated into medical curricula, and 73.12% supported mandatory AI competency training for medical students. Six AI-related competencies were identified, with “Basic Knowledge of AI” ranked the highest priority and “AI-Enhanced Clinical Encounters” was ranked the lowest in priority. Conclusion: A 2-week elective on AI in physiology was developed. This initiative aims to bridge the knowledge gap and equip future healthcare professionals with essential AI competencies.