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.

Keywords: Unified Theory of Acceptance and Use of Technology; artificial intelligence; clinical decision support systems; mental health; technology acceptance model.

Figure 1

The research model is without control variables. The model is adapted from the preuse part of the model presented in the study by Venkatesh et al [23]. In this study, we extended the original model by adding tool understanding and cognitive technology readiness as predictors of perceived usefulness, perceived ease of use, and trust. AI: artificial intelligence.

Figure 2

The output slide of the artificial intelligence (AI)–enabled feedback tool showing a visual summary of the AI-generated recommendations regarding the adherence of motivational interviewing principles.

Figure 3

The output slide of the artificial intelligence (AI)–enabled treatment recommendation tool showing a visual summary of the AI-generated mood scores for 2 patients.

Figure 4

The results of exploratory mediation analysis. Significant paths for the prediction of the intention to use the artificial intelligence (AI)–enabled (A) feedback tool and (B) treatment recommendation tool. Only nonzero paths and indirect effects are displayed.