A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff
Background: With the significant development of artificial intelligence (AI) in recent years, the inheritance and innovation of traditional Chinese medicine (TCM) urgently require the help of AI technology. The present study was to evaluate the attitudes and perceptions of medical staff towards the...
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Elsevier
2025-12-01
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| Series: | Integrative Medicine Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213422025000940 |
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| author | Yinger Gu Xinyin Hu Hye Won Lee Zheng Yao Tianyi Zhou Nv Xia Pingchun Yang Jinglu Guo Haifeng Huang Lisi Wang Wei Wang Cheng Wang Qiaoping Zhao Lingling Lou Wenjie Wu Ke Ren Guomei You Longlong Fan Jue Zhou Fangfang Wang Xiaoteng Chen Fan Qu |
| author_facet | Yinger Gu Xinyin Hu Hye Won Lee Zheng Yao Tianyi Zhou Nv Xia Pingchun Yang Jinglu Guo Haifeng Huang Lisi Wang Wei Wang Cheng Wang Qiaoping Zhao Lingling Lou Wenjie Wu Ke Ren Guomei You Longlong Fan Jue Zhou Fangfang Wang Xiaoteng Chen Fan Qu |
| author_sort | Yinger Gu |
| collection | DOAJ |
| description | Background: With the significant development of artificial intelligence (AI) in recent years, the inheritance and innovation of traditional Chinese medicine (TCM) urgently require the help of AI technology. The present study was to evaluate the attitudes and perceptions of medical staff towards the integration of TCM and AI development. Methods: A cross-sectional national survey was conducted at 13 medical institutions across China. A structured and self-reported questionnaire, consisting of six sections with a total of 14 items, was administered to 1100 medical staff between June 27th and July 11th, 2025. Results: In the process of clinical practice, 62.1 % of medical staff were willing to try TCM diagnosis and treatment services combined with AI. The top three important processes of integration of TCM and AI were medical research, personalized generation of regimen, and intelligent inquiry. The top three concerns about the potential risks associated with the integration of TCM and AI were the misinterpretation of cultural contexts, flexibility in dialectical treatment, and simplification of traditional TCM experience by algorithms. The top three most promising applications were the intelligent syndrome differentiation system (54.6 %), the TCM four diagnostic instruments (49.1 %), and the acupuncture and Tui Na robot (47.8 %). The top three most important factors in the application of AI in TCM were accuracy (78.0 %), convenience of operation (67.5 %), and participation of medical staff (60.9 %). Conclusion: The integration of TCM and AI has a brilliant and promising future, prioritizing diagnostic accuracy while addressing cultural/clinical adaptation challenges in key applications, such as syndrome differentiation systems. |
| format | Article |
| id | doaj-art-4f71b7de8a924acbab5671842d55bc19 |
| institution | Kabale University |
| issn | 2213-4220 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Integrative Medicine Research |
| spelling | doaj-art-4f71b7de8a924acbab5671842d55bc192025-08-20T05:06:47ZengElsevierIntegrative Medicine Research2213-42202025-12-0114410121410.1016/j.imr.2025.101214A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staffYinger Gu0Xinyin Hu1Hye Won Lee2Zheng Yao3Tianyi Zhou4Nv Xia5Pingchun Yang6Jinglu Guo7Haifeng Huang8Lisi Wang9Wei Wang10Cheng Wang11Qiaoping Zhao12Lingling Lou13Wenjie Wu14Ke Ren15Guomei You16Longlong Fan17Jue Zhou18Fangfang Wang19Xiaoteng Chen20Fan Qu21Department of Traditional Chinese Medicine, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaDepartment of Traditional Chinese Medicine, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaKM Convergence Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of KoreaDepartment of Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, ChinaDepartment of Traditional Chinese Medicine, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaDepartment of Outpatient, Yangming Hospital Affiliated to Ningbo University, Ningbo, ChinaDepartment of Traditional Chinese Medicine, LinCang Maternity and Child Health Care Hospital, LinCang, ChinaDepartment of Interventional Minimally Invasive Surgery, Taizhou Cancer Hospital, Taizhou, ChinaDepartment of Traditional Chinese Medicine, Dongxin subdistrict community health service, Gongshu district, Hangzhou, ChinaDepartment of Traditional Chinese Medicine Internal Medicine, Wenhui subdistrict community health service, Gongshu district, Hangzhou, ChinaDepartment of Hospital Office, Yuyao Linshan Central Health Center, Ningbo, ChinaDepartment of Hospital Office, Shengzhou Changle Town Community Health Center, Shaoxing, ChinaDepartment of Gynaecology, Shengzhou Hospital of Traditional Chinese Medicine, Shaoxing, ChinaDepartment of Physical Examination Center, Shaoxing Hospital of Traditional Chinese Medicine, Shaoxing, ChinaDepartment of Nursing, Yuyao Hospital of Traditional Chinese Medicine, Ningbo, ChinaDepartment of Hospital Office, Yuyao Mazhu Central Health Center, Ningbo, ChinaDepartment of Nursing, Zhejiang Cancer Hospital, Hangzhou, ChinaDepartment of Traditional Chinese Medicine, Aksu Prefecture Maternal and Child Health Hospital, Aksu, ChinaSchool of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, ChinaDepartment of Traditional Chinese Medicine, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaDepartment of Outpatient, Yangming Hospital Affiliated to Ningbo University, Ningbo, China; Co-corresponding authors at: Women’s Hospital, School of Medicine, Zhejiang University, No.1 Xueshi Road, Hangzhou, Zhejiang 310006, China (F. Qu); Yangming Hospital Affiliated to Ningbo University, No.800 Chengdong Road, Ningbo, Zhejiang 315400, China (X. Chen).Department of Traditional Chinese Medicine, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, China; Co-corresponding authors at: Women’s Hospital, School of Medicine, Zhejiang University, No.1 Xueshi Road, Hangzhou, Zhejiang 310006, China (F. Qu); Yangming Hospital Affiliated to Ningbo University, No.800 Chengdong Road, Ningbo, Zhejiang 315400, China (X. Chen).Background: With the significant development of artificial intelligence (AI) in recent years, the inheritance and innovation of traditional Chinese medicine (TCM) urgently require the help of AI technology. The present study was to evaluate the attitudes and perceptions of medical staff towards the integration of TCM and AI development. Methods: A cross-sectional national survey was conducted at 13 medical institutions across China. A structured and self-reported questionnaire, consisting of six sections with a total of 14 items, was administered to 1100 medical staff between June 27th and July 11th, 2025. Results: In the process of clinical practice, 62.1 % of medical staff were willing to try TCM diagnosis and treatment services combined with AI. The top three important processes of integration of TCM and AI were medical research, personalized generation of regimen, and intelligent inquiry. The top three concerns about the potential risks associated with the integration of TCM and AI were the misinterpretation of cultural contexts, flexibility in dialectical treatment, and simplification of traditional TCM experience by algorithms. The top three most promising applications were the intelligent syndrome differentiation system (54.6 %), the TCM four diagnostic instruments (49.1 %), and the acupuncture and Tui Na robot (47.8 %). The top three most important factors in the application of AI in TCM were accuracy (78.0 %), convenience of operation (67.5 %), and participation of medical staff (60.9 %). Conclusion: The integration of TCM and AI has a brilliant and promising future, prioritizing diagnostic accuracy while addressing cultural/clinical adaptation challenges in key applications, such as syndrome differentiation systems.http://www.sciencedirect.com/science/article/pii/S2213422025000940Traditional Chinese medicineArtificial intelligenceA national surveyMedical staff |
| spellingShingle | Yinger Gu Xinyin Hu Hye Won Lee Zheng Yao Tianyi Zhou Nv Xia Pingchun Yang Jinglu Guo Haifeng Huang Lisi Wang Wei Wang Cheng Wang Qiaoping Zhao Lingling Lou Wenjie Wu Ke Ren Guomei You Longlong Fan Jue Zhou Fangfang Wang Xiaoteng Chen Fan Qu A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff Integrative Medicine Research Traditional Chinese medicine Artificial intelligence A national survey Medical staff |
| title | A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff |
| title_full | A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff |
| title_fullStr | A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff |
| title_full_unstemmed | A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff |
| title_short | A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff |
| title_sort | national survey on how to improve the integration of traditional chinese medicine and artificial intelligence attitudes and perceptions from medical staff |
| topic | Traditional Chinese medicine Artificial intelligence A national survey Medical staff |
| url | http://www.sciencedirect.com/science/article/pii/S2213422025000940 |
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