ArticleNpj mental health research2026
Navigating the complexity of AI adoption in psychotherapy by identifying key facilitators and barriers.
Article in Npj mental health research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Effects of Type and Timing of Clinician-Facing AI Support on Patient Trust in Medical Consultations: 2 Vignette Experiments.Journal of medical Internet research · 2026Article
- Me, my doctor and AI: a qualitative interview study on patients' requirements for trustworthy use of AI-CDSS in ophthalmology.BMJ digital health & AI · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Artificial intelligence (AI) technologies in mental healthcare offer promising opportunities to reduce therapists' burden and enhance healthcare delivery, yet adoption remains challenging. This study identified key facilitators and barriers to AI adoption in mental healthcare, precisely psychotherapy, by conducting six online focus groups with patients and therapists, using a semi-structured guide based on the NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability) framework. Data from N = 32 participants were analyzed using a combined deductive and inductive thematic analysis. Across the seven NASSS domains, 36 categories emerged. Sixteen categories were identified as factors facilitating adoption, including useful technology elements, the customization to user needs, and cost coverage. Eleven categories were perceived as barriers to adoption, encompassing the lack of human contact, resource constraints, and AI dependency. Further nine, such as therapeutic approach and institutional differences, acted as both facilitators and barriers depending on the context. Our findings highlight the complexity of AI adoption in mental healthcare and emphasize the importance of addressing barriers early in the development of AI technologies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.