1. DETERMINANTS OF ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEMS ADOPTION AMONG MENTAL HEALTH PROFESSIONALS: THE ROLES OF EMPATHY, ACCOUNTABILITY AND TRUST

In healthcare, the rate at which technologies with artificial intelligence for use in assisting clinicians' decision-making (AI-CDSSs) are created is much faster than the rate at which these technologies become part of clinicians' clinical practice, especially among mental health clinicians. Much of the research on AI-CDSSs so far has focused on the design aspects of these technologies; however, social factors (e.g., empathy, accountability) also influence how clinicians trust and ultimately use AI-CDSSs. The purpose of the current study was to examine how the aforementioned social and technical factors influence the willingness of mental health clinicians to adopt and utilize AI-CDSS technologies. A mixed-method sequential exploratory design was employed for this study. First, a thematic synthesis of qualitative data (i.e., previous studies) was used to identify the social and technical factors of AI-CDSSs. Second, a cross-sectional survey of 309 mental health clinicians was distributed, and a partial least squares structural equation modelling (PLS-SEM) analysis was performed to test the theoretical framework. This article shows how the Technology Acceptance Model and Technology Trust are combined in the Unified Theory of Acceptance and Use of Technology (UTAUT) and how the Perceived Empathy Model (PEM) and Perceived Clarity of Accountability Model (PCA) are extensions of these models. The results indicate that Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Trusting Intention (TI), and Facilitating Conditions (FC) significantly predict the Intention to Use (IU) a computer-based Decision Support System (AI-CDSS). The findings also indicate that PEM, PCA and Perceived Reliability (PR) strongly influence Trusting Intentions (TI). Therefore, clinician adoption of AI-CDSS's will be influenced by the functionality of the technology as well as the human and contextual factors that impact the adoption of this technology. Consequently, this research will provide valuable information to improve the design of AI-CDSS's for use in mental health care.