Evidence map›Paper›PMID 41232013›Full record

ArticleJMIR medical education2025

Perceptions and Intentions to Use Generative AI Among First-Year Medical Students in Japan: Cross-Sectional Survey Study.

Hiroshi Tajima, Hajime Kasai, Kiyoshi Shikino, Ikuo Shimizu, Shoichi Ito

Abstract read
In one paragraph

Article in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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.

Hiroshi TajimaDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan, 81 43-222-7171 ext 73320, 81 43-311-3614.ORCID 0009-0004-2581-6193
Hajime KasaiDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan, 81 43-222-7171 ext 73320, 81 43-311-3614.ORCID 0000-0002-6759-2026
Kiyoshi ShikinoDepartment of Community-Oriented Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID 0000-0002-3721-3443
Ikuo ShimizuDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan, 81 43-222-7171 ext 73320, 81 43-311-3614.ORCID 0000-0002-6731-7104
Shoichi ItoDepartment of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan, 81 43-222-7171 ext 73320, 81 43-311-3614.ORCID 0000-0003-3659-6152

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: An April 2025 survey of 118 first-year Japanese medical students found high use of generative artificial intelligence (84.7%) but limited formal learning (49.2%), with strong learning interest yet neutral assignment use, indicating a need for structured literacy in generative artificial intelligence.

Indexed as

Artificial IntelligenceEducation, Medical, UndergraduateIntentionStudents, MedicalAdultCross-Sectional StudiesFemaleHumansJapanMalePerceptionSurveys and QuestionnairesYoung Adultdigital literacygenerative artificial intelligenceJapanlearning behaviormedical studentsperceptions

Identifiers

PMID41232013
PMCPMC12614389

What OpenQuestion holds

Textmetadata
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.