Evidence map›Paper›PMID 41717605›Full record

ArticleFrontiers in public health2026

The relationship between medical students' attitudes toward artificial intelligence and their personality traits: a multicenter study in China.

Jinxin Qi, Yuxiao Zeng, Hao Wang, Yuchu Xiang, Zitong Fang, Yinghan Zhang, Tingting Bao, Shuyu Yan, Lian Liu, Yaoxi Su and 2 more

Abstract readMulticenter Study
In one paragraph

Article in Frontiers in public health, 2026. 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

12 authors.

Jinxin Qi *Department of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
Yuxiao Zeng *Department of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
Hao Wang *Department of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
Yuchu Xiang *Department of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
Zitong FangDepartment of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
Yinghan ZhangWest China School of Public Health, West China Medical Center, Sichuan University, Chengdu, China.
Tingting BaoWest China School of Public Health, West China Medical Center, Sichuan University, Chengdu, China.
Shuyu YanWest China School of Medicine, Sichuan University, Chengdu, China.
Lian LiuDepartment of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
Yaoxi SuDepartment of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
Xian JiangDepartment of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
Siliang ChenDepartment of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is reshaping healthcare from clinical care and diagnostics to operations and public health and has been accelerated by recent advances in large language models (LLMs), such as ChatGPT. Because healthcare directly concerns human life and well-being, the use of AI must be approached with care. As AI systems are ultimately designed, implemented, and trusted by people, identifying the factors that shape medical students' attitudes toward AI is critical for safe and effective integration. Methods: In this study, attitudes toward AI were measured with the General Attitudes toward AI Scale (GAAIS), and personality traits were measured with the Big Five Inventory-2 (BFI-2). Along with demographic information, all these data were collected via an online platform from five-year clinical medicine students in Beijing, Shanghai, and Chengdu. Correlation and linear regression were conducted. Results: Analyses indicated that openness and agreeableness were associated with higher scores of positive attitudes toward AI, whereas conscientiousness was associated with lower scores of positive attitudes toward AI. For negative attitudes toward AI, openness and agreeableness were associated with its higher scores, whereas neuroticism was associated with its lower scores. Conclusion: These results suggest that students with greater openness and agreeableness not only view AI more favorably but are also more tolerant of its limitations. In contrast, those high in conscientiousness report fewer positive views, and those high in neuroticism are less tolerant of AI shortcomings. To our knowledge, this is the first study to examine the associations between personality traits and attitudes toward AI in medical students in China, highlighting the need for targeted educational interventions that reflect diverse personality profiles.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelPersonalityStudents, MedicalAdultChinaFemaleHumansMaleSurveys and QuestionnairesYoung Adultartificial intelligencebig five inventorygeneral attitudes toward AI scalemedical educationmedical students

Identifiers

PMID41717605
PMCPMC12913472

What OpenQuestion holds

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Registered trials

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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.