Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China
Abstract Objectives To investigate the attitudes of Chinese radiologists or interns towards generative pre-trained (GPT)-like technologies. Methods A prospective survey was distributed to 1339 Chinese radiologists or interns via an online platform from October 2023 to May 2024. The questionnaire cov...
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| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-025-01908-8 |
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| author | Tianyi Xia Shijun Zhang Ben Zhao Ying Lei Zebin Xiao Bingwei Chen Junhao Zha Yaoyao Yu Zhijun Wu Chunqiang Lu Tianyu Tang Yang Song Yuancheng Wang Shenghong Ju |
| author_facet | Tianyi Xia Shijun Zhang Ben Zhao Ying Lei Zebin Xiao Bingwei Chen Junhao Zha Yaoyao Yu Zhijun Wu Chunqiang Lu Tianyu Tang Yang Song Yuancheng Wang Shenghong Ju |
| author_sort | Tianyi Xia |
| collection | DOAJ |
| description | Abstract Objectives To investigate the attitudes of Chinese radiologists or interns towards generative pre-trained (GPT)-like technologies. Methods A 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. Results After 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). Conclusions Chinese 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. Graphical Abstract |
| format | Article |
| id | doaj-art-16990371a5a24988a7b737da146e5469 |
| institution | Kabale University |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj-art-16990371a5a24988a7b737da146e54692025-02-02T12:27:56ZengSpringerOpenInsights into Imaging1869-41012025-01-0116111410.1186/s13244-025-01908-8Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in ChinaTianyi Xia0Shijun Zhang1Ben Zhao2Ying Lei3Zebin Xiao4Bingwei Chen5Junhao Zha6Yaoyao Yu7Zhijun Wu8Chunqiang Lu9Tianyu Tang10Yang Song11Yuancheng Wang12Shenghong Ju13Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityDepartment of Biomedical Sciences, University of PennsylvaniaDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityMR Research Collaboration Team, Siemens Healthineers Ltd.Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityAbstract Objectives To investigate the attitudes of Chinese radiologists or interns towards generative pre-trained (GPT)-like technologies. Methods A 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. Results After 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). Conclusions Chinese 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. Graphical Abstracthttps://doi.org/10.1186/s13244-025-01908-8RadiologistsAttitudeSurveys and questionnairesNatural language processing |
| spellingShingle | Tianyi Xia Shijun Zhang Ben Zhao Ying Lei Zebin Xiao Bingwei Chen Junhao Zha Yaoyao Yu Zhijun Wu Chunqiang Lu Tianyu Tang Yang Song Yuancheng Wang Shenghong Ju Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China Insights into Imaging Radiologists Attitude Surveys and questionnaires Natural language processing |
| title | Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China |
| title_full | Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China |
| title_fullStr | Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China |
| title_full_unstemmed | Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China |
| title_short | Attitudes of radiologists and interns toward the adoption of GPT-like technologies: a National Survey Study in China |
| title_sort | attitudes of radiologists and interns toward the adoption of gpt like technologies a national survey study in china |
| topic | Radiologists Attitude Surveys and questionnaires Natural language processing |
| url | https://doi.org/10.1186/s13244-025-01908-8 |
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