Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study

Abstract

Background Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care. Objective This study aimed to address this gap by examining the predictors of psychology students’ and early practitioners’ intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology. Methods This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools’ functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions. Results Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed. Conclusions The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care.

Understanding AI Adoption: The Mediating Role of Attitude in User Acceptance

“…Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) on Behavioural Intention to Use (BIU) of AI tools Perceived usefulness (PU) and Perceived Ease of Use (PEOU) play crucial roles in influencing the Behavioral Intention to Use AI tools in mental healthcare (Kleine et al, 2023). Research on information technology in various sectors, such as Micro, Small, and Medium Enterprises (MSMEs) and the banking industry, also supports the significance of PU and PEOU in determining behavioral intention to use technology (Samsuryaningrum & Rahayu, 2022;Widiar et al, 2023;Kurnia et al, 2023).…”

“…Research on AI-driven chatbots in banking systems and AI-enabled mental health tools highlights the importance of ATU in determining BIU (Saxena et al, 2023;Ching & Kwok, 2022). Additionally, studies on Technology Enhanced Learning (TEL) tools and AI attitudes and usage intentions further emphasize the impact of ATU on BIU, showcasing its dominance as a determinant of intention to use technological tools (Kleine et al, 2023). Understanding and fostering a positive attitude towards AI tools are essential for promoting their successful adoption and utilization in various domains.…”

Predictors of Healthcare Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems (AI-CDSSs): A Meta-Analysis Based on the Universal Theory of Acceptance and Use of Technology (UTAUT) (Preprint)

“…If healthcare professionals experience anxiety in using AI-CDSSs, their intention to use them is presumably low. Indeed, AI anxiety has been identified as a negative predictor of the intention to use AI in healthcare [23].…”

“…Research to identify predictors of the intention to use AI-CDSSs has accumulated over the past years [3,[22][23][24]. However, the existing literature remains scattered and in predictors and use intention, thus shedding light on the conditions that influence the strength of these relationships.…”

AI-Enabled Clinical Decision Support Tools for Mental Healthcare: A Product Review

“…On the other hand, AI-enabled clinical decision support systems (AI-CDSSs) can be implemented in clinical settings to assist practitioners in providing better care for their patients. AI-CDSSs for mental healthcare may be used to detect or predict the onset or development of conditions, or to predict patient responses to treatments (Aafjes-van Doorn et al, 2021;Aleem et al, 2022;Chekroud et al, 2021;Cho et al, 2019;Kleine, Kokje, et al, 2023;Popescu et al, 2021).…”