AI-Based Approaches to Refugee Mental Health Care: Systematic Integrative Review
Abstract
Background: Refugees and forcibly displaced populations experience elevated rates of mental health conditions, including posttraumatic stress disorder, depression, and anxiety, while facing substantial barriers to mental health care. AI has emerged as a promising approach for mental health detection, intervention, and decision support; however, no review has specifically examined AI-based approaches to refugee mental health care.
Objective: This systematic integrative review aimed to (1) examine how AI has been used in refugee mental health research and care, (2) synthesize the reported strengths and limitations of AI-based approaches, and (3) identify future directions for this field.
Methods: An integrative review methodology was used following Whittemore and Knafl's framework. Systematic searches were conducted across PubMed, Embase, CINAHL, PsycINFO, Scopus, Web of Science, Cochrane Library, ACM Guide to Computing Literature, IEEE Xplore, arXiv, and medRxiv from inception through June 2026. The first 100 results from Google Scholar sorted by relevance were additionally screened. Studies were included if they focused on refugee populations and applied AI technologies to refugee mental health care or assessment, while studies focusing on voluntary migrants and digital mental health not using AI were excluded. Quality appraisal was performed mainly using the Mixed Methods Appraisal Tool. Data were synthesized in accordance with the integrative review methodology.
Results:…
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