Evidence map›Paper›PMID 41820954›Full record

ArticleBMC medical education2026

AI knowledge, attitudes, perceptions, and willingness to use AI among oncologists in China: a nationwide cross-sectional study.

Xiaomin Xiong, Mingming Xiao, Haixia Liu, Chunling Jiang, Ming Li, Yongmei Song, Jing Zhou, Bo Xu, China Anti-Cancer Association on Artificial Intelligence in Oncology

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. 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

9 authors.

Xiaomin XiongSchool of Medicine, Chongqing University, Chongqing, China.
Mingming XiaoDepartment of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Haixia LiuCardiology Department, School of Medicine, Chongqing University Cancer Hospital, Chongqing University, Chongqing, 400000, China.
Chunling JiangDepartment of Radiation Oncology, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, Nanchang, 330029, China.
Ming LiJohns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, USA.
Yongmei SongState Key Laboratory of Molecular Oncology, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. symlh2006@163.com.
Jing ZhouWest China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China. jingzhou556@163.com.
Bo XuSchool of Medicine, Chongqing University, Chongqing, China. xubo731@cqu.edu.cn.
China Anti-Cancer Association on Artificial Intelligence in Oncology

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe integration of artificial intelligence (AI) into healthcare, particularly in oncology, has attracted considerable attention in China. Understanding oncologists' views on AI is essential for developing effective integration strategies in oncology settings. This study aimed to evaluate AI knowledge, attitudes, perceptions, and willingness among nationwide oncologists in China. Additionally, we identified factors influencing oncologists' willingness to use AI.

methodsA nationwide cross-sectional survey of 1,538 oncologists from 31 regions in China was conducted between August and November 2023 using a 22-item online questionnaire, launched on the Wenjuan online platform and shared by members of Chinese Anti-Cancer Association via WeChat.

resultsThe survey achieved an 87.78% completion rate (1,350/1,538). Among the respondents, 90% had used medical AI products, and 14.59% had used ChatGPT. Respondents demonstrated different levels of understanding of AI terms (mean = 3.524), limitations (mean = 3.256), trust (mean = 3.759), and acceptance (mean = 4.022), with scores ranging from 1 (low) to 5 (high) on 5-point Likert items. Perceptions of AI surpassing doctors' diagnostic abilities had a mean score of 3.255, whereas perceptions of AI replacing doctors had a mean score of 2.829. Both perceptions, along with willingness to use AI (mean = 4.162) and support for its widespread use (mean = 4.061), were also measured on 5-point Likert scale. Factors associated with high willingness to use AI included senior clinical titles (OR 1.41 [95% CI: 1.05-1.90]), working in the radiation oncology department (OR 1.9 [95% CI: 1.29-2.81]), high understanding AI terms (OR 4.66 [95% CI: 3.46-6.34]), prior experience with ChatGPT (OR 2.28 [95% CI: 1.32-4.07]), and high scores on items such as "AI had already surpassed doctors' diagnostic abilities" (OR 8.82 [95% CI: 1.82-2.56]) or "AI will replace doctors" (OR 13.32 [95% CI: 2.11-3.13]).

conclusionsThis study unveils a good understanding and positive attitudes toward AI, coupled with a strong willingness to use AI products among nationwide oncologists across China, despite ongoing controversy over whether AI will replace doctors. Those with senior titles or greater AI knowledge and experience show a high willingness to use AI. Considering the findings, targeted AI education and increased exposure are crucial for the successful integration of AI into oncology settings in China.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelHealth Knowledge, Attitudes, PracticeOncologistsAdultChinaCross-Sectional StudiesFemaleHumansMaleMedical OncologyMiddle AgedSurveys and QuestionnairesArtificial intelligenceAttitudesChinaCross-sectional surveyKnowledgeOncologistsPerceptionsWillingness to use

Identifiers

PMID41820954
PMCPMC13063491

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.