ArticleDigital health
Analysis of clinical doctors' adoption willingness pathway for the artificial intelligence-based clinical decision support system (AI-CDSS)-A survey study from China.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A process model of psychological acceptance in technology-based service innovation: a symbolic interactionism perspective.Frontiers in psychology · 2026Article
- Identifying Measurement Dimensions of Users' Benefit-Risk Perceptions of AI in Healthcare: A Scoping Review.Inquiry : a journal of medical care organization, provision and financingArticle
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Authors and funding
5 authors.
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Abstract
Background: In China, clinical doctors bear heavy medical responsibilities and perform large volumes of diagnostic and therapeutic tasks. As the Artificial Intelligence-based Clinical Decision Support System (AI-CDSS) can assist in diagnosis, treatment, and decision-making to improve healthcare quality, it is especially important for improving healthcare quality. However, a crucial question remains: Are clinical doctors willing to adopt the AI-CDSS? Methods: Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and related theoretical models, this study constructed an influence factor model for the willingness of clinical doctors to adopt the AI-CDSS. This model includes seven dimensions: performance expectancy, perceived risk, facilitating conditions, social influence, technology anxiety, personal innovativeness, and adoption willingness. Following the outline, a survey questionnaire was designed and distributed to 450 clinical doctors across 27 Chinese provinces. A structural equation model was used to analyse the factors influencing AI-CDSS adoption willingness, and semi-structured interviews were conducted to supplement and explain the data results. Results: A significant positive association was found between AI-CDSS adoption willingness and performance expectancy (β=0.149, P < 0.001), social influence (β=0.156, P < 0.001), and personal innovativeness (β=0.649, P < 0.001). Conversely, technology anxiety demonstrated a significant negative association (β=-0.142, P < 0.001), while perceived risk showed no significant association (P = 0.115). This study also evaluated the moderating effects of the institutional level and AI-CDSS usage experience. Increased social influence was found to slightly weaken doctors' willingness to adopt the AI-CDSS in tertiary hospitals. Conclusions: This study constructed a theoretical influence factor model of AI-CDSS adoption willingness that is applicable to the context of China. Simultaneously, the study uncovers the double-edged sword mechanism of social influence in driving the willingness to adopt an AI-CDSS. This provides significant insights for clinical doctors, policymakers, medical institution managers, and AI-CDSS developers and aids in promoting the adoption of AI-CDSS in China.
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