SynthesisFrontiers in artificial intelligence2025
Impact of generative AI in medical education in India: a systematic review.
Synthesis in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Review
- Artificial intelligence readiness and its association with artificial intelligence literacy among Chinese medical students: a latent profile analysis.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The advent of generative Artificial Intelligence (AI) has presented a fundamental change in the approach to medical education across the world. In India, where the medical education is facing a shortage in faculties and resources, generative AI (GenAI) has the potential of transforming this. This systematic review summarizes the current evidence on the impact, student readiness, and various ethical challenges and barriers of integration of AI into the medical curriculum. Methods: We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and searched published articles in PubMed and Google Scholar from 2020 to 2025. The search yielded 19,777 articles, from which 11 studies focusing on Indian medical students were selected. The findings of these studies were analyzed using Laurillard's six learning modes to gain a comprehensive pedagogical understanding. Result: Our study revealed a significant finding: while high awareness and positive perception towards AI have been shown by Indian medical students, most of the students lack formal training. These selected studies show that the students mostly use generative AI for clearing doubts, making assignments, and self-directed learning, shifting from the 'Acquisition' to 'Inquiry' and 'Production' modes of Laurillard's learning. Comparative Analysis showed that GenAI tools outperform students on standard exams, thus showing their potential. However, certain challenges also exist, including the risk of misinformation, over-reliance, potential decrease in critical thinking, and ethical concerns of data privacy. Conclusion: Indian medical students are enthusiastically adopting GenAI, but their engagement is mostly unstructured and informal. A significant gap exists between the readiness of the students and the medical institutions. To maximize the potential use of GenAI, our institutions have to develop a structured curriculum, invest in faculty training, and establish ethical guidelines. Teamwork between policymakers, educators, and researchers is the need of the hour so that our future physicians will be ready to integrate AI-enabled healthcare. Syestematic review registration: https://doi.org/10.17605/OSF.IO/2MJVK.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.