Increasing acceptance of medical AI: The role of medical staff participation in AI development

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2023-07, Vol.175, p.105073-105073, Article 105073
• Main Authors: Huo, Weiwei, Yuan, Xinze, Li, Xianmiao, Luo, Wenhao, Xie, Jiaying, Shi, Bowen
• Format: Article
• Language: English
• Subjects: Acceptance of artificial intelligence; AI anxiety; AI self-efficacy; Artificial Intelligence; COVID-19; Humans; Medical Staff; Medical staff participation; Pandemics; Speciesism; Work Engagement
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2023.105073

•Extending acceptance of medical AI on the medical staff participation perspective.•Medical staff participation impacts acceptance of AI through the dual path.•AI self-efficacy plays a mediating role between staff participation and acceptance of AI.•AI anxiety plays a mediating role between staff participation and acceptance of AI-ADT.•Speciesism moderates the effect of medical staff participation on the AI anxiety. Medical artificial intelligence (AI) in varying degrees has exerted significant influence on many medical fields, especially in the midst of the COVID-19 pandemic. However, little is known regarding how to address the reluctance of medical staff to use AI technology. While recent research has highlighted the importance of medical staff participation in the development of AI, the current understanding of influence of medical staff participation on acceptance of AI is limited. To provide insights into the mechanism that how medical staff participation impacts on the medical staff’s acceptance of AI and to examine the moderating effect of speciesism. This study was conducted from 6th August to 3rd September. Data was collected from doctors and nurses and a total of 288 valid questionnaires were obtained. Smart PLS 3.2.8 was used as partial least square (PLS) software to validate the research model. The study determined that medical staff participation had a significant impact on acceptance of medical AI-IDT (β = 0.35, p ≤ 0.001) and acceptance of medical AI-ADT (β = 0.44, p ≤ 0.001). The results also show that AI self-efficacy and AI anxiety have significant mediating effects and speciesism has significant moderating effects among the theoretical model. This study provides insights into ways to explore influence factors of acceptance of AI based on user participation perspective. The results indicate that medical staff participation enhances acceptance of medical AI through the cognitive path (i.e., AI self-efficacy) and the affective path (i.e., AI anxiety). These results have practical implications for how organizations assist the staff to accommodate themselves to AI technology in the future.