# Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis

## TL;DR

This review evaluates how artificial intelligence is used to improve psychosocial functioning in people with psychosis, finding moderate accuracy but highlighting the need for better methods and consistency.

## Contribution

The study is the first systematic review and meta-analysis focusing on AI applications specifically for psychosocial functioning in psychosis.

## Key findings

- AI models showed moderate accuracy in predicting psychosocial outcomes, with a pooled AUC of 0.70 and RMSE of 8.15.
- Higher predictive accuracy was found for social cognition (AUC=0.77) and clinical symptom-based predictors (RMSE=7.1).
- Methodological variability and data quality issues limit the reliability and generalizability of AI applications in this area.

## Abstract

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.

A literature search was conducted using the PubMed, Scopus, and ACM Digital Library databases for articles published between January 2010 and March 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.

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.

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.

https://www.crd.york.ac.uk/prospero/, identifier CRD420251051952.