North Indian trainees show limited AI knowledge and interest in training

An exploratory cross-sectional survey of 194 medical students and resident doctors at a tertiary care teaching hospital in North India found that most participants rated their artificial intelligence knowledge as poor to average. The study also found that formal exposure to AI was uncommon, more than 80% of respondents reported limited or no familiarity with ethical and data-governance issues, and a large majority expressed interest in structured AI education. The results provide a baseline picture of self-reported knowledge, exposure, tool use and attitudes at one institution rather than evidence across India’s medical-training system.

The questionnaire covered MBBS students and resident doctors: 113 participants were medical students and 81 were resident doctors. Researchers asked respondents about their knowledge of AI, exposure to it within the medical curriculum, current use of AI tools, awareness of ethical considerations and attitudes toward future training. They analyzed the responses descriptively and used non-parametric statistical tests for comparisons between students and residents. Knowledge ratings did not differ significantly between the two groups, with a Mann–Whitney U value of 4853 and a p-value of 0.764.

ChatGPT was the most frequently used AI tool among respondents. Educational and academic purposes were the main reported uses, including language correction, literature review and manuscript preparation. The survey establishes that ChatGPT was the tool most often named and describes the activities in which it was used; it does not quantify how often participants used it or show that use of AI improved learning, research quality or clinical performance. Ethical and data-governance awareness was substantially lower: over 80% of respondents said they had limited familiarity with these issues or none at all.

Despite their limited formal exposure and generally modest self-assessed knowledge, a large majority of participants said they were interested in structured education about AI and its implications. The authors therefore presented the findings as baseline evidence for integrating AI-related competencies, including ethical and data-governance considerations, into medical training programs. Because the survey was questionnaire-based and cross-sectional, it captured reported attitudes and experiences at a single point in time. It did not test an educational intervention, measure changes over time, compare institutions or establish effects on clinical care, and its results should not be generalized beyond the surveyed trainees without further evidence.

Subject of Research: Self-reported AI knowledge, use, ethics awareness and training interest among medical students and resident doctors at a North Indian teaching hospital

Article Title: Artificial intelligence in medicine: a cross-sectional study of knowledge and attitudes

Article References: Gupta, I., Garg, A., Varadarajan, A., Jain, S., & Wig, N. (2026). Artificial intelligence in medicine: a cross-sectional study of knowledge and attitudes. BMC Medical Education. https://doi.org/10.1186/s12909-026-10052-w

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10052-w

Keywords: artificial intelligence, medical education, medical students, resident doctors, ChatGPT, AI ethics, data governance, cross-sectional survey, North India, Artificial, intelligence, medicine