AI in Medicine: Uncovering the Educational Pulse Through Students’ Perspective

Background AI is rapidly transforming healthcare delivery and medical training. While global bodies advocate for incorporating AI competencies into medical curricula, limited data exist on the perceptions, knowledge levels, ethical concerns, and training needs of Indian medical students. Understanding student perspectives is vital for designing future-ready curricula. Methodology A descriptive cross-sectional study was conducted among MBBS students at Government Medical College, Nagpur, between August and September 2024. A pretested, semi-structured, self-administered Google Form questionnaire (Google LLC, Mountain View, CA, USA) assessed perceptions of AI utility, ethical concerns, self-rated knowledge, and preferences for AI training. A convenience sample was obtained by inviting ~50 students from each academic phase (response rate: 94.4%, n = 236/250). Data were analyzed using IBM SPSS Statistics for Windows, version 25.0 (released 2017; IBM Corp., Armonk, NY, USA). Ethical approval was obtained from the Institutional Ethics Committee, and informed consent was collected digitally. Results A total of 236 students participated (mean age 20.52 ± 1.47 years; 53% male). Most respondents expressed positive perceptions of AI’s potential to enhance decision-making, improve precision, and support healthcare accessibility (≥60% agreement). However, concerns about erosion of humanistic care, compromise of patient-physician relationships, and data privacy breaches were prominent. Only 31 students (13.1%) had received prior AI training. Students without training were significantly more likely to believe that AI threatens physician employment (71.2% vs. 35.5%; p = 0.00042), whereas trained students viewed AI as an augmentative tool and felt more confident about using AI in future practice (p < 0.00001). Self-rated knowledge was low, with 66.5% reporting no or minimal familiarity. A substantial majority (78.8%) expressed willingness to receive AI training. Students strongly supported integrating practical AI skills, ethics, research applications, predictive modeling, and clinical decision-support tools into the curriculum (≥60% endorsement across domains). Online learning was the preferred mode (38.1%). Conclusions Medical students demonstrate high enthusiasm for AI but possess limited knowledge and significant ethical concerns. Prior exposure to AI training correlates with more informed, positive perceptions of AI as a supportive rather than a replacement technology. The strong demand for a structured AI curriculum highlights the need for integrating foundational, technical, and ethical AI competencies into undergraduate medical education. A balanced, context-sensitive curriculum emphasizing both technological literacy and humanistic values is essential for preparing future physicians for AI-enabled healthcare.