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SYSTEMATIC REVIEW article

Front. Psychiatry

Sec. Digital Mental Health

Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1692177

This article is part of the Research TopicTransdiagnostic approach in studying mental health conditions: The contribution of fundamental and translational brain research for precise interventionsView all 3 articles

Application of Artificial Intelligence and Psychosocial Functioning in Psychosis: A Systematic Review and Meta-analysis

Provisionally accepted
  • 1The University of Hong Kong, Pokfulam, Hong Kong, SAR China
  • 2Kwai Chung Hospital, Kowloon, Hong Kong, SAR China

The final, formatted version of the article will be published soon.

Abstract Introduction: Artificial intelligence (AI) has emerged as a valuable tool in mental health care, with applications in the treatment of psychosis. However, its application to psychosocial functioning in psychosis remains underexplored, despite its critical role towards long-term therapeutic outcomes and recovery. The goal of this systematic review and meta-analysis is to identify, summarize, and evaluate the current evidence on AI applications in psychosocial functioning in psychosis. Methods: A literature search was conducted using the PubMed, Scopus, and ACM Digital Library databases for articles published between January 2010 and May 2025, in accordance with the PRISMA guidelines. Quality of studies was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST), Newcastle-Ottawa Scale (NOS), and the Cochrane Risk of Bias Tool (RoB2.0). Meta-analyses synthesized commonly used performance metrics using random-effects models, with subgroup, sensitivity and publication bias analyses. Results: A total of 14 studies were included in this review. Various AI techniques were employed, with supervised machine learning being the most predominant. Psychosocial domains, including social function, occupational function, social cognition and quality of life, were targeted. Meta-analysis revealed moderate discriminative and predictive accuracies of AI models: pooled AUC of 0.70 (95% CI: 0.63–0.76) and RMSE of 8.15 (95% CI: 7.32–8.98). Subgroup analyses indicated higher predictive accuracy for social cognition (AUC=0.77) and clinical symptom-based predictors (RMSE=7.1), with substantial heterogeneity mainly attributed to methodological variability. 1 Conclusions: This review discovered the current application of AI in psychosocial functioning in psychosis, including the techniques usage, modeling approaches, targeted domains, and model performance. AI showed promise for early identification, continuous monitoring, and personalized interventions, driven by methodological advances such as ensemble learning with feature selection. Nevertheless, limitations in methodological consistency, data quality, model design, and ethical issues underscore that the field remains in its early stages. Overall, AI should complement clinical expertise, rather than replace it, in delivering psychosocial care in psychosis. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD420251051952.

Keywords: AI, artificial intelligence, machine learning, psychosis, psychosocial functioning

Received: 25 Aug 2025; Accepted: 20 Oct 2025.

Copyright: © 2025 Mok, Cheng and Chu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Calvin Pak Wing Cheng, chengpsy@hku.hk

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