Authors (including presenting author) :
Chloe HYM (1) (2), Calvin CPW (1), Menza CHW (2)
Affiliation :
(1) Department of Psychiatry, The University of Hong Kong, Hong Kong SAR
(2) Department of Occupational Therapy, Kwai Chung Hospital, Hong Kong SAR
Keyword 2: :
Artificial Intelligence
Keyword 3: :
Machine Learning
Keyword 5: :
Psychosocial Functioning
Keyword 6: :
Predictive Modelling
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 in long-term therapeutic outcomes and recovery.
Objectives :
To identify, summarize, and evaluate the current evidence on AI applications in psychosocial functioning in psychosis.
Methodology :
A literature search was conducted using PubMed, Scopus, and the ACM Digital Library for articles published between January 2010 and March 2025, in accordance with PRISMA guidelines. The quality of studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), the 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.
Result & Outcome :
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.
