Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology

Artificial intelligence–enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited. The meta-analysis identified predictors influencing health care practitioners’ intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence.

Financial stress and quit intention: the mediating role of entrepreneurs’ affective commitment

One primary reason entrepreneurs abandon their goals is due to financial difficulties. In one experimental and two field studies, we found a positive relationship between financial stress and quit intention, mediated by affective commitment to their entrepreneurial endeavors. The findings are in line with the challenge–hindrance stressor (CHS) framework and self-determination theory (SDT).

Challenge and threat appraisal of entrepreneurial errors: a latent profile analysis and examination of coping responses

According to transactional stress theory (TST), entrepreneurs’ coping strategies depend on viewing errors as challenges or threats. This study uses latent profile analysis to explore distinct profiles of challenge and threat appraisals among entrepreneurs. The findings reveal five appraisal profiles that highlight differences in error damage control and rumination, suggesting improvements for TST and error management interventions.

Advancing Mental Health Care with AI-Enabled Precision Psychiatry Tools: A Patent Review

We wrote a review on AI-enabled precision psychiatry patents published between 2015 and mid-October 2022. Multiple analytic approaches, such as graphic network analysis and topic modeling, are used to analyze the scope, content, and trends of the retained patents. The tools described aim to provide diagnosis, prediction of treatment responses, and prognosis of mental disorder symptoms. Additionally, about one-third of the tools suggest treatment options related to selection, adjustment, and management. The complexity of technology combinations has increased over the years. This review highlights the potential of AI-enabled precision psychiatry tools for adoption in practice.