Evidence map›Paper›PMID 39890713›Full record

ArticleInsights into imaging2025

Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China.

Tianyi Xia, Shijun Zhang, Ben Zhao, Ying Lei, Zebin Xiao, Bingwei Chen, Junhao Zha, Yaoyao Yu, Zhijun Wu, Chunqiang Lu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 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

14 authors.

Tianyi Xia *Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Shijun Zhang *Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Ben ZhaoNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Ying LeiNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Zebin XiaoDepartment of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA, USA.
Bingwei ChenDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China.
Junhao ZhaNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Yaoyao YuNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Zhijun WuNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Chunqiang LuNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Tianyu TangNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Yang SongMR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China.
Yuancheng WangNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Shenghong JuNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China. jsh@seu.edu.cn.ORCID http://orcid.org/0000-0001-5041-7865

Funding

National Natural Science Foundation of China 82330060National Natural Science Foundation of China 82371940National Natural Science Foundation of China 823B2040National Natural Science Foundation of China 82427803National Natural Science Foundation of China 92359304
6 · The paper itself

Abstract

objectivesTo investigate the attitudes of Chinese radiologists or interns towards generative pre-trained (GPT)-like technologies.

methodsA prospective survey was distributed to 1339 Chinese radiologists or interns via an online platform from October 2023 to May 2024. The questionnaire covered respondent characteristics, opinions on using GPT-like technologies (in clinical practice, training and education, environment and regulation, and development trends), and their attitudes toward these technologies. Logistic regression was conducted to identify underlying factors associated with the attitude.

resultsAfter quality control, 1289 respondents (median age, 37.0 years [IQR, 31.0-44.0 years]; 813 males) were surveyed. Most of the respondents (n = 1223, 94.9%) supported adoption of GPT-like technologies. Based on the acceptance level of GPT-like technologies, the respondents were 3 (0.2%), 29 (2.2%), 352 (27.3%), 677 (52.5%), and 228 (17.7%) from low to high acceptance degrees. Multivariable analysis revealed significant associations between positive attitudes towards GPT-like technologies and their acceptance: writing papers and language polishing (odds ratio [OR] = 1.99; p < 0.001), influence of colleagues using such technologies (OR = 1.77; p = 0.007), government regulation introduction (OR = 2.25; p < 0.001), and enhancement of decision support capabilities (OR = 2.67; p < 0.001). Sensitivity analyses confirmed these results for different acceptance thresholds (all p < 0.001).

conclusionsChinese radiologists or interns generally support GPT-like technologies due to their potential capabilities in clinical practice, medical education, and scientific research. They also emphasize the need for regulatory oversight and remain optimistic about their future medical applications. CRITICAL RELEVANCE STATEMENT: This study highlights the broad support among Chinese radiologists for GPT-like technologies, emphasizing their potential to enhance clinical decision-making, streamline medical education, and improve research efficiency, while underscoring the need for regulatory oversight. KEY POINTS: The impact of GPT-like technologies on the radiology field is unclear. Most Chinese radiologists express the supportive adoption of GPT-like technologies. GPT-like technologies' capabilities at research and clinic prompt the attitude.

Indexed as

AttitudeNatural language processingRadiologistsSurveys and questionnaires

Identifiers

PMID39890713
PMCPMC11785863

What OpenQuestion holds

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LicenceCC BY
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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.