This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement.
Abstract
Abstract Digital mental health has become an established part of mental health care, but the rapid arrival of large language models and other artificial intelligence (AI) tools has refocused attention on the evidence needed to guide the field. This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement. While the importance of these priorities has not changed in recent years, the urgency with which they must now be applied has. As digital tools become more clinically consequential, research must move beyond demonstrating that a technology is feasible, usable, or novel. The field now needs studies that clarify how these tools work, for whom they are beneficial, under what conditions they may cause harm, and how they can be ethically integrated into care. We call for research that is transparent about the technologies being studied, grounded in meaningful clinical questions, attentive to safety, and designed to produce knowledge that remains useful as specific products and models change.
Artificial intelligence (AI) has entered psychiatry at scale, yet its clinical impact remains constrained by a sizable gap between technical validation and real-world implementation. The central barriers are no longer computational, but infrastructural: unreliable measurement systems, incomplete governance frameworks, and insufficient standards for clinical evidence and integration. This paper synthesizes insights from a 2026 American College of Neuropsychopharmacology (ACNP) study group examining how to responsibly translate AI into clinical mental health care. Building on this perspective, we outline five priorities required for clinical impact. First, robust measurement and phenotyping infrastructure, such as reliable psychometrics, digital phenotyping, and standardized data pipelines, is essential for clinically meaningful AI. Second, the most immediate and scalable impact of AI lies in clinician-facing augmentation tools that reduce workflow burden, such as ambient documentation systems and structured decision-support pipelines, with important research to conduct here. Third, patient-facing AI interventions show promise but require rigorous safety evaluation, particularly for implicit suicide risk and heterogeneous treatment effects. Fourth, governance and equity frameworks must extend beyond privacy to address bias, digital literacy, and research integrity. Fifth, future progress requires moving beyond predictive models toward causal, mechanistic approaches to precision psychiatry that better inform treatment decisions and clinical action. Together, these priorities define a translational agenda for 2026 and beyond: AI in mental health will succeed not through model performance alone, but through disciplined integration into clinical workflows, measurement systems, and governance structures that ensure safety, equity, and real-world effectiveness. This paper synthesizes insights from a 2026 American College of Neuropsychopharmacology (ACNP) study group examining how to responsibly translate AI into clinical mental health care. Building on this perspective, we outline five priorities required for clinical impact. These priorities define a translational agenda for 2026 and beyond: AI in mental health will succeed not through model performance alone, but through disciplined integration into clinical workflows, measurement systems, and governance structures that ensure safety, equity, and real-world effectiveness.
Martin P. Paulus, J. Torous, R. Perlis et al.· NPP—Digital Psychiatry and N...· 0 citations
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.
K. Moon, Matthew Tamura, J. R. Redmond et al.· Frontiers in Psychiatry· 0 citations
Background Artificial intelligence (AI) is increasingly used for mental health support, through both purpose-built products and general-purpose large language models (LLMs), at a pace far greater than evidence of their efficacy or safety. There is no consensus taxonomy of the potential harms arising from this use, and existing classification frameworks are fragmented across clinical, computational, ethical, and regulatory domains. Objective This scoping review aims to systematically map the harms which may arise from engaging with AI for mental health support, as formulated in the academic literature, regulatory frameworks, grey literature, and practitioner guidance, to inform the co-production of a harm taxonomy. Methods We will follow the framework of Arksey and O’Malley, as refined by Levac et al. and the Joanna Briggs Institute, with reporting adhering to the PRISMA Extension for Scoping Reviews (PRISMA-ScR). We will search MEDLINE (Ovid), Embase (Ovid), APA PsycINFO (EBSCOhost), Scopus, Web of Science, ACM Digital Library, IEEE Xplore, ProQuest Dissertations and Theses Global, PROSPERO, and Google Scholar for peer-reviewed literature and preprints, combining terms for AI technologies, mental health, and harm. Grey literature searches will cover regulatory bodies, professional associations, the Mental Health Innovation Network, OpenSyllabus, and preprint repositories. No date restriction will be applied; searches will be restricted to English. Titles, abstracts, and full texts will be screened independently by two reviewers following a calibration exercise. Data will be charted using a piloted standardised instrument and synthesised narratively through descriptive tabulation and iterative harm mapping, with consultation of lived experience advisors and clinical experts before finalisation. Ethics and dissemination No primary data will be collected, so ethical approval is not required. Findings will be published as a peer-reviewed manuscript, presented at conferences, and used to inform co-production workshops within the SAFER-MH programme.
X. Hunt, A. G. Mokaya, Sara Zannone et al.· Wellcome Open Research· 0 citations
Large language models (LLMs) are rapidly becoming embedded in everyday mental health help-seeking practices, particularly among young people who already turn to digital platforms as gateways to mental health support. While LLMs offer unprecedented immediacy and accessibility, their integration into help-seeking ecosystems raises important questions for digital health research. This opinion paper argues that LLMs fundamentally reshape the developmental processes underpinning online help-seeking. Traditional digital help-seeking requires active exploration, searching, comparing sources and reflecting on lived experience, processes that contribute to mental health literacy and resilience. In contrast, LLMs collapse informational plurality into singular, authoritative-sounding responses, potentially shifting users from active exploration toward passive consumption. We discuss the risks of sycophancy, and over-reliance on immediacy, and consider how these dynamics may alter developmental trajectories of coping and help-seeking agency. We argue that preserving agency, connectedness, and reflective engagement must be central to the design of conversational AI in health contexts.
Claudette Pretorius· Information Hiding· 0 citations
Overall, findings suggest that current systems remain insufficiently adapted to the MENA context, underscoring the need for culturally grounded, dialect-sensitive, and clinically supervised approaches to ensure safe and effective integration.
Sara El Hajj, Ahmad Nsouli, M. Wehbe et al.· Frontiers in Psychiatry· 0 citations
A list of eight best practices was created to assist developers with designing AI systems in a way that would reduce the overall risk of harm for users attempting to use their AI for mental health cases.
Joshua Frankenfield, Briana M. Sobel, Barbara Chaparro· Proceedings of the Internati...· 0 citations
