Nine in Ten Medical Students at a Peruvian University Report Using AI Tools

A cross-sectional survey of undergraduate medical students at Universidad Ricardo Palma in Lima, Peru, has found that 257 of 278 respondents — 92.4 percent — reported previously using artificial intelligence tools for academic activities. The observational, analytical study, conducted in 2024 and published in Frontiers in Artificial Intelligence, offers one of the first structured snapshots of how future physicians in Peru engage with AI in their day-to-day studies.

The research team, led by Karen Jackeline Aviles Antezana, distributed an adapted online questionnaire based on previously published instruments. Of 279 responses received, one respondent did not provide informed consent and was excluded, leaving 278 participants for analysis. AI use was assessed through a separate yes/no question asking whether the student had previously used any AI tool, a design choice intended to capture self-reported exposure rather than infer it from answers about specific applications.

Beyond the headline prevalence figure, the survey documented widespread familiarity with the technology. Most participants self-reported basic knowledge of AI principles, at 89.2 percent, and an even larger share — 95.3 percent — said they were aware of AI’s use in medicine. Among predefined categories of AI tools, ChatGPT was the most frequently reported, cited by 69.1 percent of respondents, followed by the design platform Canva at 19.8 percent.

The authors emphasize that the study’s primary contribution is descriptive: it documents the prevalence and patterns of AI use, self-reported knowledge, and perceptions within this specific surveyed population. Cross-sectional surveys of this kind capture a single moment in time at a single institution, so the figures describe these students rather than establishing trends or generalizing to medical students across Peru or elsewhere.

A secondary, exploratory layer of the analysis used regression models to probe which characteristics were associated with AI use. These suggested links with daily digital-device use and with perceptions regarding treatment planning. The authors caution, however, that these estimates should be interpreted carefully: only 21 participants were classified as non-users, leaving the comparison group small and the statistical footing for the associations correspondingly fragile.

That imbalance between users and non-users is a common challenge in technology-adoption research. When more than nine in ten respondents already use the tools under study, the contrast group becomes too thin to support robust inference, and apparent associations can shift substantially with small changes in the data. The authors flag this limitation explicitly rather than presenting the regression results as settled findings.

The study arrives amid rapid uptake of generative AI in higher education, where large language models and content-generation platforms have become embedded in coursework, note-taking, and study preparation. Medical education faces particular questions in this shift, since students must learn not only how to use these tools but also how to evaluate their outputs critically, understand their limitations, and consider ethical dimensions such as accuracy, privacy, and responsible use in a clinical context.

Consistent with that concern, the authors frame their findings as highlighting the need for structured education addressing critical, ethical, and responsible AI use in undergraduate medical training.

The survey’s scope remains modest: one university, one academic year, and self-reported answers to an adapted questionnaire. Yet the near-universal prevalence of AI use among these students — and the concentration of that use around a single conversational tool — gives the findings practical weight for educators designing AI-related instruction.

Subject of Research: Self-reported AI use, knowledge, and perceptions among undergraduate medical students in Peru

Article Title: Self-reported artificial intelligence use, knowledge, and perceptions among undergraduate medical students at a Peruvian university

Article References: Avilés-Antezana, K., Soto, A., Hernández-Patiño, I., Vela-Ruiz, J. M., & Lama-Valdivia, J. (2026). Self-reported artificial intelligence use, knowledge, and perceptions among undergraduate medical students at a Peruvian university. Frontiers in Artificial Intelligence, 9, Article 1940222. https://doi.org/10.3389/frai.2026.1940222

Image Credits: AI Generated

DOI: 10.3389/frai.2026.1940222

Keywords: artificial intelligence, medical education, Peru, cross-sectional study, ChatGPT, survey, digital health, technology adoption, undergraduate students, self-reported knowledge, Self-reported, artificial