Harms associated with engaging with AI for mental health support: A scoping review protocol
TL;DR
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.
Abstract
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.