ArticleJMA journal2025
Use and Evaluation of Generative Artificial Intelligence by Medical Students in Japan.
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 3 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
3 citing papers in PubMed.
- Performance of Large Language Models in the Japanese Public Health Nurse National Examination: Comparative Cross-Sectional Study.JMIR nursing · 2026Article
- Article
- Medical Education and Artificial Intelligence: Some Suggestions.JMA journal · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Generative artificial intelligence (AI) has become more accessible due to technological advancements. While it can support more efficient learning, improper use may lead to legal issues or hinder self-directed learning. Medical education is no exception, as generative AI has the potential to become a powerful tool. However, its practicality remains uncertain. Therefore, we investigated how generative AI is perceived among medical students and utilized within the realm of medical education. Methods: In January 2024, we conducted a study with 123 second-year medical students who had completed a physiology course and laboratory training at Gunma University, Japan. Students used ChatGPT (Chat Generative Pre-trained Transformer) 3.5 (OpenAI) for four tasks and evaluated its responses. A survey on the use of generative AI was also conducted. Responses from 117 participants were analyzed, excluding six non-participants. Results: Among the students, 41.9% had used ChatGPT. The average scores for tasks 1-4 were 6.5, 4.6, 7.4, and 6.2 out of 10, respectively. Although 13% had a negative impression, 54 students found it challenging to apply for medical purposes. However, 64.1% expressed a willingness to continue using generative AI, provided its use extended beyond medical contexts. Conclusions: Nearly 60% of students had never used generative AI before, which is consistent with general usage trends. Although they were impressed by the speed of generative AI responses, many students found that it lacked precision for medical studies and required additional verification. Limitations of generative AI, such as "hallucinations," were evident in medical education. It remains important to educate students on AI literacy and their understanding of the potential issues that generative AI could bring about.
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What OpenQuestion holds
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.