AI Chatbots Surge in Indian Medical Education: 86% of MBBS Students Rely on Tools Like ChatGPT, Mumbai Study Reveals

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A groundbreaking study from Mumbai reveals that nearly all MBBS students are aware of AI chatbots like ChatGPT, with 86% actively using them for academic purposes such as exam preparation and research support. Conducted at Hinduhridaysamrat Balasaheb Thackeray Medical College between June and July 2025, the research surveyed 385 undergraduates and underscores a rapid digital shift in medical training amid global debates on AI’s role. Published in the prestigious Indian Journal of Medical Research (IJMR), these findings highlight both opportunities and challenges as educators grapple with integrating AI ethically.

Key Findings from the Study

The cross-sectional survey found 97.7% awareness of AI chatbots among participants aged 18-24, with males comprising 54.8% of respondents. Exam preparation topped usage at 69.4%, followed by research support (47.5%) and mnemonic creation (28.8%), primarily via smartphones. Over 90% reported reliable internet access, yet only 35.6% understood prompt engineering—techniques to craft effective queries for better AI outputs—indicating room for deeper training.

Students accessed chatbots daily to occasionally, valuing quick information retrieval and personalized scenarios akin to having a 24/7 study companion. More than 60% deemed them valuable supplements to lectures and e-books, supporting curricular inclusion. However, barriers like technical skill gaps and device limitations persisted, with usage uneven across years but higher among third- and final-year students.

Global Context and Comparisons

This Indian data aligns with international trends, where a Chinese study reported 28.72% active ChatGPT use but broad recognition of efficiency gains. In the US, chatbots aid clinical reasoning, performing comparably to third-year students on USMLE questions, while Saudi research noted higher female and senior-year adoption despite accuracy worries. UK mixed-methods work showed cautious optimism, echoing concerns over ethics and reliability seen in Europe.

Indian precedents exist, with students praising AI for condensing vast syllabi but stressing critical evaluation. Globally, systematic reviews praise flexibility and instant feedback, yet flag factual errors or “hallucinations”—AI’s tendency to invent details. ChatGPT-assisted problem-based learning has boosted engagement in trials, suggesting AI augments, not replaces, mentorship.

Expert Perspectives

Dr. Abhijat Sheth, NMC Chairperson and NBEMS President, emphasizes AI’s healthcare role but warns against over-dependence: “AI must complement, not replace doctors and should never compromise ethical or clinical values.” He referenced NBEMS’s free AI course for trainees, promoting responsible use.

Dr. Srk, corresponding author from the Mumbai study , notes in IJMR: “The results underscore the need for training in prompt engineering and responsible AI use.” External experts like those in JMIR Medical Education agree, advocating customizable chatbots for safe clinical simulations while cautioning on privacy and overreliance. “AI enhances outcomes but risks eroding interpersonal skills if unchecked,” a Frontiers review adds.

Public Health and Educational Implications

For India’s overburdened medical system, AI could democratize learning, aiding self-directed study in resource-scarce settings and preparing future doctors for tech-driven practice. Practical takeaways include using chatbots for scenario practice—like generating cardiology cases—to build reasoning, always cross-verifying outputs against textbooks. Over 60% student support signals readiness for AI literacy modules in curricula, potentially improving retention via tailored feedback.

Broader impacts span public health: Better-trained physicians could enhance diagnostics and equity, aligning with NMC’s push for research integration via ICMR-IIT collaborations. Globally, AI promises personalized care, but India’s context—90% smartphone access—positions it as a leader if regulated.

Limitations and Counterarguments

Self-reported data risks bias, and the single-institution focus limits generalizability beyond urban Mumbai. Cross-sectional design misses long-term effects, like dependency on AI stunting critical thinking. Counterviews highlight risks: factual inconsistencies could mislead learners, and ethical lapses—like unverified advice—demand guidelines.

Regulation divides opinions; while students favor inclusion, experts urge policies on data privacy and output validation. Longitudinal studies are needed to assess if AI boosts or hinders clinical competence. Balanced integration—treating chatbots as tools, not tutors—remains key.

Medical Disclaimer: This article is for informational purposes only and should not be considered medical advice. Always consult with qualified healthcare professionals before making any health-related decisions or changes to your treatment plan. The information presented here is based on current research and expert opinions, which may evolve as new evidence emerges.