ArticleFrontiers in psychiatry2026
Facts label for transparent communication of AI Risks in mental health technology.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With interest in the adoption of artificial intelligence (AI)-enabled digital mental health technologies (AI-DMHTs) among the general population ceaselessly escalating, mental health clinicians are obliged to confront questions about their utility and safety for their practice. However, little guidance exists for developers on how to communicate risks to clinicians who may need to evaluate products for individuals with mental health concerns, individuals who are frequently vulnerable to such risks. We propose a standardized facts label for AI-DMHTs designed to enhance transparency and awareness about these tools and their risks to users, patients, and clinicians. This framework was developed by a multidisciplinary team from the American Psychiatric Association Committee on Mental Health Information Technology through iterative expert review and external clinician consultation, drawing upon existing scholarship in risk communication and informed consent, as well as international AI governance frameworks. The resulting facts label framework is composed of 8 sections: key identifying information, intended use, warnings, risks and limitations, model information, clinical evidence, accessibility and usability considerations, and privacy and security. This research represents a practical step toward responsible utilization of AI-DMHTs and aims to serve as a foundation for continued multidisciplinary collaboration regarding the development and governance of AI risk communication in the domain of mental health and healthcare more broadly.
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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.