Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review
Clinical Snapshot
PICO Framework
| P — Population | Individuals with schizophrenia requiring rehabilitation management |
| I — Intervention | Artificial intelligence applications for rehabilitation purposes |
| C — Comparator | Standard care or non-AI rehabilitation approaches |
| O — Outcomes | Rehabilitation outcomes including symptom monitoring, medication management, risk management, functional training, and psychosocial support |
Bottom Line
This scoping review of 83 studies reveals that AI applications in schizophrenia rehabilitation show promise for symptom monitoring and medication adherence support, with median classification performance of AUC 0.79. However, the evidence base has significant limitations: only four studies reported external validation, three described real-world deployment, and just one randomized trial demonstrated clinical benefit. Applications heavily favor monitoring over active rehabilitation interventions like functional training. While relapse prediction models achieve high specificity (88%), sensitivity remains modest (31.5%). The predominance of proxy over clinical endpoints, sparse calibration reporting, and limited fairness auditing raise concerns about real-world implementation. For Australian clinicians, these findings suggest AI tools may complement existing rehabilitation approaches but require rigorous local validation, integration with established care pathways, and human oversight before routine deployment.
Key Findings
P Value: Not reported for pooled analyses
Effect Size: Median AUC 0.79 across classification tasks
Primary Outcome: AI applications predominantly focused on symptom monitoring (48/83 studies) and medication management (19/83 studies)
Nnt Or Sensitivity: Relapse early-warning models: median sensitivity 31.5% at 88.0% specificity
Confidence Interval: IQR 0.71-0.86 for AUC values
Clinical Application
Limited by sparse external validation and minimal closed-loop deployment evidence Relevant to Australian mental health services but would require local validation studies and integration with existing RACGP mental health guidelines and PBS medication management protocols Individuals with schizophrenia in rehabilitation settings, particularly those requiring monitoring and adherence support
Abstract
Artificial intelligence (AI) is increasingly used in mental health, yet its rehabilitation-oriented applications in schizophrenia have not been systematically mapped. We conducted a systematic scoping review of PubMed, Web of Science, IEEE Xplore and the ACM Digital Library (January 1, 2012-October 31, 2025; two search rounds), applying operationalized rehabilitation boundaries and excluding diagnostics-only case-control studies. We extracted data on data sources, feature engineering, model families, validation, calibration, interpretability, application domains, outcomes and implementation readiness. Eighty-three studies met inclusion criteria (median sample size 160; 55% longitudinal). Applications focused on symptom monitoring (48/83), medication management (19/83) and risk management (16/83), whereas functional training (1/83) and psychosocial support (3/83) were rarely targeted. Supervised learning predominated (53/83, 63.9%) over representation learning (20/83, 24%), most commonly using speech/text, electronic health records and smartphone sensing. Across classification tasks, the median AUC was 0.79 (IQR 0.71-0.86); relapse early-warning models showed a median sensitivity of 31.5% at 88.0% specificity. Only four studies reported external validation and three described closed-loop deployment, including one randomized trial that improved adherence. Proxy endpoints were more common than clinical endpoints, and reporting of calibration/uncertainty and fairness auditing was sparse. Overall, AI shows promise for monitoring, adherence support and relapse risk stratification, but routine-care deployment will require externally validated and calibrated human-in-the-loop decision support, privacy-preserving multimodal pipelines and pragmatic trials targeting functional outcomes and participation.
References
- 1.Yang, H., Chang, F., Muroi, F., Liu, Z., Zhang, W., & Cai, J. (2026). Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review. Translational Psychiatry, 14, Article 248. https://doi.org/10.1038/s41746-024-01248-9
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