ArticleJMA journal2025
Rapidly Rising Preference among New-entry Medical Students for Using Generative Artificial Intelligence in Reflective Reports.
Article in JMA journal, 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A qualitative thematic comparative analysis of independent and AI-supported clinical reflections among medical interns.Frontiers in medicine · 2026Article
- Use and perception of generative artificial intelligence in traditional Korean medicine education: A cross-sectional survey of undergraduate students in Korea.Integrative medicine research · 2025Article
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
Introduction: As artificial intelligence (AI) continues to proliferate, it becomes imperative that medical students are not only instructed in the use of AI but also afforded regular opportunities to interact with it throughout their medical education. In 2023 and 2024, an art-based reflective reports assignment was implemented among new-entry students of the medical program at a university in Japan. The assignment required students to use generative AI within their submissions. The objective of this study was to assess the perceptions of these students regarding the use of generative AI in their reflective reports over the course of two academic years. Methods: First-year medical students were tasked with submitting an art-based reflective report about their experiences during the community-based inquiry learning session. The assignment was a component of their reflective competencies assessment. We conducted a survey of the 2023 and 2024 student cohorts and examined their perceptions of using generative AI using a five-point Likert scale. We compared the survey responses of the two cohorts using the t-test. Results: The participants exhibited a notably higher mean score for positive perceptions toward generative AI in 2024 compared with 2023 (3.8 versus 2.9 points; p < 0.01). In addition, the proportion of participants who expressed a preference for using generative AI was significantly higher in 2024 than in 2023 (32.0% versus 17.4%; p = 0.02). Conclusions: A substantial and rapid increase has been observed in the proportion of medical students indicating a preference for using generative AI. The joint adoption of generative AI by medical students and faculties signifies a significant and urgently required development in AI use within medical education.
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Registered trials
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.