Evidence map›Paper›PMID 40918762›Full record

ArticleCureus2025

Awareness and Attitudes Toward Artificial Intelligence Language Generation Models in Medical Education: A Cross-Sectional Questionnaire Study Among Medical Students in Southern China.

Min Zhang, Tao Liu, Xiang Peng, Yuanhan Chen, Min Zhi

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Min ZhangDepartment of Gastroenterology, Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, CHN.
Tao LiuDepartment of Gastroenterology, Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, CHN.
Xiang PengDepartment of Gastroenterology, Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, CHN.
Yuanhan ChenDivision of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University, Guangzhou, CHN.
Min ZhiDepartment of Gastroenterology, Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, CHN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose To evaluate the feasibility of artificial intelligence language generation models (AILMs) in medical education, we examined the utilization patterns and attitudes of medical students in a developed area of Southern China. Methods We conducted a cross-sectional questionnaire survey assessing educational background, awareness, usage, and attitudes towards AILMs. Attitudes were measured using a five-point Likert scale, where scores of 4 or above indicated support, scores of 2 or below indicated opposition, and a score of 3 indicated a neutral stance. Results Among the 254 respondents, the average awareness score for AILMs was 2.4. AILMs were primarily used for solving medical and academic problems. Although students were aware of many domestic AILM products, foreign products were preferred. More than half of the students used AILMs less than once a week, and 13 (5.1%) students had never used them. A significant portion supported the integration of AILMs in current (187/254, 73.6%) and future (194/249, 78.0%) education, with a strong correlation between these attitudes (

Indexed as

artificial intelligence language generation modelsattitudefeasibility studymedical educationmedical studentsquestionnaire survey

Identifiers

PMID40918762
PMCPMC12410506

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.