Evidence map›Paper›PMID 40786463›Full record

ArticleJMA journal2025

Use and Evaluation of Generative Artificial Intelligence by Medical Students in Japan.

Izuki Amano, Kisho Obi-Nagata, Ayane Ninomiya, Yuki Fujiwara, Noriyuki Koibuchi

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Izuki AmanoDepartment of Integrative Physiology, Gunma University Graduate School of Medicine, Maebashi, Japan.
Kisho Obi-NagataDepartment of Integrative Physiology, Gunma University Graduate School of Medicine, Maebashi, Japan.
Ayane NinomiyaDepartment of Integrative Physiology, Gunma University Graduate School of Medicine, Maebashi, Japan.
Yuki FujiwaraDepartment of Integrative Physiology, Gunma University Graduate School of Medicine, Maebashi, Japan.
Noriyuki KoibuchiDepartment of Integrative Physiology, Gunma University Graduate School of Medicine, Maebashi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligenceChatGPTgenerative AIhallucinationlarge language model

Identifiers

PMID40786463
PMCPMC12328371

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.