Determinants of physicians’ intention to use AI-assisted diagnosis: An integrated readiness perspective

Artificial intelligence (AI) in medical imaging applications improves the effectiveness and efficiency of disease diagnosis in medical image analysis through neural networks, machine learning tools, and big data. However, previous studies have only explained the impact of AI medical applications on the healthcare process, providing clinical diagnoses, and recommending treatments; therefore, they are insufficient to explain physician AI-assisted diagnosis adoption behavior. The present study has proposed a theoretical model to explain physicians' AI-assisted diagnosis adoption behavior. The resulting 237 valid questionnaires constituted a response rate of 72.48%. The results indicated that functional value, social value, emotional value, and epistemic value had positive effects on personal involvement. The results also showed that functional value, emotional value, personal involvement, optimism, and innovativeness had a positive influence on physicians' intentions to adopt AI-assisted diagnosis. The results also found that perceived unregulated standards and perceived mistrust have negative effects on behavior intentions. The results provide unique insights that will not only assist hospital administrators in developing an appropriate AI-assisted diagnosis implementation strategy but also enable software developers, medical imaging manufacturers, and government agencies to develop and appropriate their own marketing and administrative strategies for the future.