Aug 2026· Wellcome Open Research· Vol 11, pp. 542· 0 citations· 55 references
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.
ABSTRACT Artificial intelligence (AI) is increasingly being integrated into healthcare systems and has the potential to improve health outcomes. In Sub-Saharan Africa (SSA), however, concerns remain that AI may either reduce or exacerbate existing health inequities depending on how it is developed, governed, and implemented. This scoping review aimed to map and synthesise the existing evidence on the implications of AI for health equity among marginalised populations in Sub-Saharan Africa. PubMed, Web of Science, Scopus, and selected grey literature sources were searched between February and March 2026. Peer-reviewed and grey literature examining AI applications, governance, or implementation in healthcare involving marginalised populations or health systems within SSA were eligible for inclusion. The review followed the Arksey and O’Malley methodological framework and the PRISMA-ScR reporting guideline. Two reviewers independently screened sources of evidence and extracted data using a standardised charting form, and findings were synthesised thematically. Twenty-three sources of evidence met the inclusion criteria. Two dominant narratives emerged. AI may reinforce existing inequities through digital infrastructure gaps, algorithmic bias, under-representation of African datasets, weak governance, and data colonialism. Conversely, AI has the potential to improve health equity by expanding healthcare access, strengthening disease surveillance, supporting health system planning, and improving access to specialised services. Across the literature, AI’s impact consistently depended on equitable infrastructure, inclusive governance, and context-specific implementation. AI has considerable potential to advance health equity in SSA. However, achieving equitable benefits requires investment in digital infrastructure, representative data systems, ethical governance, and inclusive policies.
Talent Tapera, T. Mgutshini· Global Health Action· 0 citations
This review provides a clinician-centered understanding of AI adoption, highlighting that acceptability depends not only on what AI can do but also on whether it can be integrated safely, ethically, and in ways that preserve professional judgment and therapeutic relationships.
Carly Hudson, Thuy Linh Phan, Marcus Randall· Journal of Medical Internet...· 0 citations
The findings underscore the need for responsible AI governance, robust clinical oversight, multidisciplinary collaboration, multidisciplinary collaboration, and longitudinal research to better understand and mitigate the long-term mental health consequences of generative AI.
Heidi Heather Henry Heimbruch, D. Eke, Lauren Henry et al.· Journal of Life Science and...· 0 citations
Artificial intelligence has significant potential to enhance mental health nursing by supporting early identification of symptoms, improving access to care and strengthening clinical decision-making, with stronger evidence observed in reviews evaluating AI interventions for early detection and risk prediction.
J. Odame, Gabriel Obeng-Gyamfi, Dayeon Heo et al.· Journal of Psychiatric and M...· 0 citations
Artificial intelligence (AI) is transforming mental health care by enabling novel approaches to assessment, outcome prediction, and treatment. However, the rapid growth of AI tools has outpaced evidence synthesis on their realworld clinical value, implementation barriers, and ethical risks, leaving clinicians and policymakers without clear guidance for responsible integration. This narrative review aimed to examine current AI applications in mental health care, identify their clinical benefits, implementation barriers, and ethical challenges, and define conditions for responsible integration that preserve the patient-clinician relationship. A structured literature search of PubMed, PsycINFO, and Scopus was conducted for peer reviewed English language articles published between January 2019 and March 2026. Studies addressing AI applications, benefits, implementation, or ethics in mental health were included and synthesised narratively into thematic categories. The review found that AI tools—for example, selfreferral chatbots that reduced waiting times and increased treatment uptake—provide expanded 24/7 access, improved clinical efficiency, and potential for individualised personalisation. Predictive models showed promise for treatment selection and risk stratification, while natural language processing unlocked unstructured clinical data. However, concerns included patient data privacy, algorithmic bias that may worsen existing inequities, potential erosion of the therapeutic relationship, and mixed acceptance among clinicians and patients. Many AI applications remain experimental, and regulatory frameworks have not kept pace with technological developments. This review did not include formal quality appraisal or quantitative synthesis; the evidence is limited by short followup periods, high dropout in chatbot trials, and a predominance of studies from highincome countries. Future research should employ longitudinal, codesigned, mixedmethods designs and pragmatic trials that evaluate clinical outcomes alongside equity, user trust, and the preservation of empathic, humanled care—rather than relying solely on uncontrolled implementation studies. AI should be responsibly integrated to augment, not replace, the clinical workforce. Successful application requires a balance between technological advancement, patient protection, and preservation of the patient-clinician relationship.
Shizal Nawaz, Laiba Nawaz, Hasnain Ali et al.· Digital Medicine· 0 citations
Objective This scoping review aimed to examine the current available literature on the use of artificial intelligence (AI) scribes across all medical fields compared to their use in psychiatry. Methods A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Five electronic databases (PubMed, MEDLINE, EMBASE, PsycINFO, and CINAHL) were systematically searched for studies published up to June 2025. Two reviewers independently screened titles and abstracts according to pre-established eligibility criteria using the PICOS framework. Full-text articles were then assessed for inclusion. Results A total of 1896 records were identified, of which 15 met the inclusion criteria. Most studies were conducted in outpatient, primary care, and procedural settings. Across studies, clinicians generally reported favorable acceptance and perceptions of AI scribes. Findings also suggested improved clinician well-being, reduced documentation time, better workflow efficiency, and positive patient experience. Results on documentation accuracy and quality were mixed. Critically, no eligible studies evaluated the use of AI scribes in psychiatry. Limitations included study heterogeneity, with wide variations in study design, sample size, and evaluation metrics. Conclusion The absence of empirical studies in psychiatry highlights a significant gap in the literature, particularly in contrast to the growing body of research across other medical specialties. Because of its unique clinical, ethical, and relational dimensions, dedicated research is needed to evaluate the feasibility, safety, and ethical implications of implementing AI scribes in psychiatric care.
Chloé Daignault, Justine Audelin-Rinfret, M. Désilets et al.· Santé mentale au Québec· 0 citations
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