Evidence map›Paper›PMID 40883732›Full record

ArticleBMC medical education2025

Application and ethical implication of generative artificial intelligence in medical education: a cross-sectional study among critical care academic physicians in China.

Yuankai Zhou, LiuFeng Zhao, Liangyu Mi, Shengjun Liu, Yingying Yang, Qianlin Wang, Huaiwu He, Yun Long

Abstract read
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Yuankai ZhouDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
LiuFeng ZhaoArtificial Intelligence Laboratory, Big Data Academy ZhongKe, Zhengzhou, China.
Liangyu MiDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Shengjun LiuDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Yingying YangDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Qianlin WangDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Huaiwu HeDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Yun LongDepartment of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China. ly_icu@aliyun.com.

Funding

National High Level Hospital Clinical Research Funding 2022-PUMCH-B-115
6 · The paper itself

Abstract

backgroundThis study explored the use of Generative Artificial Intelligence (GAI) by Chinese academic physicians in clinical teaching activities within standardized residency training (TA-SRT) in critical care settings, and their awareness of ethical issues associated with GAI. The findings of this study will guide the rationale for GAI applications and sustainable integration into medical education.

methodsThis nationwide cross-sectional study utilized a self-administered questionnaire distributed via the Wenjuanxing platform to attending and higher-ranking physicians in critical care medicine departments across China who were involved in TA-SRT. Data were collected from December 2024 to January 2025.

resultsAmong 456 enrolled academic physicians, 64.7% used GAI in clinical medical work and 33.1% used it in TA-SRT (GAI-User). GAI-Users reported higher training levels of GAI (p < 0.0001) and were more supportive of increased GAI training (p = 0.0368 and 0.0064, respectively). They also showed greater trust in the GAI judgment (p < 0.0001) and optimism regarding its application. Top GAI uses included querying teaching content (79.5%) and creating teaching materials (65.6%). Over 50% of physicians were aware of ethical issues, with “over-reliance” (85.7%) and “data privacy” (84.0%) being major concerns. Most agreed ethics should be part of GAI education (94.3%).

conclusionsAlthough awareness of GAI is growing among Chinese academic physicians in critical care, its practical use in clinical teaching remains limited. These academic physicians acknowledge GAI’s potential to enhance clinical teaching quality and promote educational equity. To foster broader adoption, cultivating the clinical application skills of GAI is essential to lay the foundation for its educational use. Additionally, GAI training programs should integrate both practical application skills and ethical education, which are widely supported by academic physicians, to guide the rationale and sustainable integration of GAI into medical education.

Indexed as

Critical CareEducation, MedicalGenerative Artificial IntelligenceInternship and ResidencyAcademiaAdultAttitude of Health PersonnelChinaCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesCritical care medicineEthicsGenerative artificial intelligenceMedical educationStandardized residency training

Identifiers

PMID40883732
PMCPMC12395772

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.