AI-enabled clinical decision support tools for mental healthcare: A product review.

The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare. From 84 potential products, seven fulfilled the inclusion criteria. The products can be categorized into three major areas: diagnosis of autism spectrum disorder (ASD) based on clinical history, behavioral, and eye-tracking data; diagnosis of multiple disorders based on conversational data; and medication selection based on clinical history and genetic data.

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.

Background: 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.

Objective: This 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.

Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist.

Background And Aim: Skin cancer is a major public health issue. While self-examinations and professional screenings are recommended, they are rarely performed. Mobile health (mHealth) apps utilising artificial intelligence (AI) for skin cancer screening offer a potential solution to aid self-examinations; however, their uptake is low.

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

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.

Personal condition but social cure: Agentic ingroups elevate well-being in chronically ill patients through perceptions of personal control.

Objectives: Social-cure research has shown that ingroup identification can be beneficial for personal health and well-being. Initial evidence for healthy participants suggests that this might be due to group membership providing a sense of personal control. In this research, we investigate this pathway for chronically ill patients, assuming that any ingroup (even patient identity) can serve as social cure by increasing control as long as the ingroup is perceived as agentic (i.

Psychological interventions targeting partners of cancer patients: A systematic review.

Purpose: Cancer patients' intimate partners often experience levels of psychological burden that are comparable to or even exceed that of the patients, making it imperative that they too be provided with appropriate psychological support. This review aimed to present the content and the effects of interventions delivered to caregiving partners of cancer patients on both partners and patients. Furthermore, we provide information about the acceptability of the interventions and study quality.