Healthcare providers' perceptions of artificial intelligence in diabetes care: A cross-sectional study in China

Yongzhen Mo; Fang Zhao; Li Yuan; Qiuling Xing; Yingxia Zhou; Quanying Wu; Caihong Li; Juan Lin; Haidi Wu; Shunzhi Deng; Mingxia Zhang

doi:10.1016/j.ijnss.2025.04.013

Healthcare providers' perceptions of artificial intelligence in diabetes care: A cross-sectional study in China

Int J Nurs Sci. 2025 Apr 28;12(3):218-224. doi: 10.1016/j.ijnss.2025.04.013. eCollection 2025 May.

Authors

Yongzhen Mo¹, Fang Zhao², Li Yuan³, Qiuling Xing⁴, Yingxia Zhou⁵, Quanying Wu⁶, Caihong Li⁷, Juan Lin⁸, Haidi Wu⁹, Shunzhi Deng¹⁰, Mingxia Zhang¹¹

Affiliations

¹ Comprehensive Geriatric Assessment Research Center, Geriatric Hospital of Nanjing Medical University, Jiangsu, China.
² Department of Nursing, China-Japan Friendship Hospital, Beijing, China.
³ Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Sichuan, China.
⁴ NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
⁵ Department of Endocrinology and Metabolism, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶ Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
⁷ Department of Nursing, Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
⁸ Department of Nursing, Fujian Provincial Hospital Affiliated to Fuzhou University, Fujian, China.
⁹ Department of Endocrinology and Metabolism, Geriatric Hospital of Nanjing Medical University, Jiangsu, China.
¹⁰ School of Nursing, Medical College of Soochow University, Jiangsu, China.
¹¹ Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.

Abstract

Objectives: Diabetes remains a major global health challenge in China. Artificial intelligence (AI) has demonstrated considerable potential in improving diabetes management. This study aimed to assess healthcare providers' perceptions regarding AI in diabetes care across China.

Methods: A cross-sectional survey was conducted using snowball sampling from November 12 to November 24, 2024. We selected 514 physicians and nurses by a snowball sampling method from healthcare providers across 30 cities or provinces in China. The self-developed questionnaire comprised five sections with 19 questions assessing medical workers' demographic characteristics, AI-related experience and interest, awareness, attitudes, and concerns regarding AI in diabetes care. Statistical analysis was performed using t-test, analysis of variance (ANOVA), and linear regression.

Results: Among them, 20.0 % and 48.1 % of respondents had participated in AI-related research and training, while 85.4 % expressed moderate to high interest in AI training for diabetes care. Most respondents reported partial awareness of AI in diabetes care, and only 12.6 % exhibited a comprehensive or substantial understanding. Attitudes toward AI in diabetes care were generally positive, with a mean score of 24.50 ± 3.38. Nurses demonstrated significantly higher scores than physicians (P < 0.05). Greater awareness, prior AI training experience, and higher interest in AI training in diabetes care were strongly associated with more positive attitudes (P < 0.05). Key concerns regarding AI included trust issues from AI-clinician inconsistencies (77.2 %), increased workload and clinical workflow disruptions (63.4 %), and incomplete legal and regulatory frameworks (60.3 %). Only 34.2 % of respondents expressed concerns about job displacement, indicating general confidence in their professional roles.

Conclusions: While Chinese healthcare providers show moderate awareness of AI in diabetes care, their attitudes are generally positive, and they are considerably interested in future training. Tailored, role-specific AI training is essential for equitable and effective integration into clinical practice. Additionally, transparent, reliable, ethical AI models must be prioritized to alleviate practitioners' concerns.

Keywords: Artificial intelligence; Attitudes; Diabetes; Medical workers; Nursing; Perceptions.