ArticleBMC health services research2025
Mental health practitioners' perceptions and adoption intentions of AI-enabled technologies: an international mixed-methods study.
Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in mental health care: a scoping review of reviews.Frontiers in psychiatry · 2026Pooled it
- Patient experience and psychologists' attitudes toward AI in psychological treatment: A topic-modeling study.Internet interventions · 2026Article
- Generative AI Adoption in Psychotherapy Practice Among Mental Health Professionals: Multicountry Cross-Sectional Survey.JMIR mental health · 2026Article
- Clinicians' Attitudes and Perceptions on the Adoption of AI in Mental Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial intelligence in psychiatric care and education: a qualitative study of factors influencing adoption in Singapore.International journal of medical education · 2026Article
- Use of a Conversational Agent for Training Mental Health Professionals in Suicide Safety Planning: Pilot Feasibility and Acceptability Study.JMIR mental health · 2026Article
- Mapping Practice-Based Signals of Generative AI in Psychiatric Care: Qualitative Study of Korean Psychiatrists' Experiences, Interpretations, and Implementation Priorities.Journal of medical Internet research · 2026Article
- Exploring Students' Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar.Healthcare (Basel, Switzerland) · 2026Article
- Heterogeneity in nurses' attitudes toward artificial intelligence: a latent profile analysis.BMC health services research · 2026Article
- Navigating the complexity of AI adoption in psychotherapy by identifying key facilitators and barriers.Npj mental health research · 2026Article
- Heterogeneity in AI attitudes, anxiety, and acceptance among psychology students and psychotherapy trainees: a domain-specific latent class analysis.Frontiers in psychology · 2026Article
- Psychiatrists' and trainees' knowledge, perception, and readiness for integration of artificial intelligence in mental health care in Nigeria.BMC psychiatry · 2025Article
- Who Wants to Have an AI Therapist? Acceptance of Using Artificial Intelligence for Mental Health Interventions Among Clinicians, Patients and the General Community.Clinical psychology & psychotherapyArticle
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Authors and funding
4 authors.
Funding
Abstract
backgroundAs mental health disorders continue to surge, exceeding the capacity of available therapeutic resources, the emergence of technologies enabled by artificial intelligence (AI) offers promising solutions for supporting and delivering patient care. However, there is limited research on mental health practitioners' understanding, familiarity, and adoption intentions regarding these AI technologies. We, therefore, examined to what extent practitioners' characteristics are associated with their learning and use intentions of AI technologies in four application domains (diagnostics, treatment, feedback, and practice management). These characteristics include medical AI readiness with its subdimensions, AI anxiety with its subdimensions, technology self-efficacy, affinity for technology interaction, and professional identification.
methodsMixed-methods data from N = 392 German and US practitioners, encompassing psychotherapists (in training), psychiatrists, and clinical psychologists, was analyzed. A deductive thematic approach was employed to evaluate mental health practitioners' understanding and familiarity with AI technologies. Additionally, structural equation modeling (SEM) was used to examine the relationship between practitioners' characteristics and their adoption intentions for different technologies.
resultsQualitative analysis unveiled a substantial gap in familiarity with AI applications in mental healthcare among practitioners. While some practitioner characteristics were only associated with specific AI application areas (e.g., cognitive readiness with learning intentions for feedback tools), we found that learning intention, ethical knowledge, and affinity for technology interaction were relevant across all four application areas, underscoring their relevance in the adoption of AI technologies in mental healthcare.
conclusionIn conclusion, this pre-registered study underscores the importance of recognizing the interplay between diverse factors for training opportunities and consequently, a streamlined implementation of AI-enabled technologies in mental healthcare.
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