Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: A Systematic Scoping Review

TL;DR

This systematic review evaluates how artificial intelligence is currently used in schizophrenia rehabilitation, highlighting predominant methods, key application areas, and future research directions to enhance patient care.

Contribution

It provides a comprehensive overview of AI applications in schizophrenia management, emphasizing supervised learning techniques and identifying future research opportunities.

Findings

Supervised machine learning dominates symptom monitoring and relapse risk management.

AI applications span symptom monitoring, medication, risk, functional training, and psychosocial support.

Future directions include multimodal data integration and deep learning models.

Abstract

This systematic review assessed the current state and future prospects of artificial intelligence (AI) in schizophrenia rehabilitation management. We reviewed 61 studies on AI-related data types, feature engineering methods, algorithmic models, and evaluation metrics published from 2012-2024. The review categorizes AI applications into the following key application areas: symptom monitoring, medication management, risk management, functional training, and psychosocial support. Findings indicate that supervised machine learning techniques, particularly for symptom monitoring and relapse risk management, remain the predominant approaches, effectively leveraging structured data while incorporating interpretable algorithms. This study underscores the potential of AI in transforming long-term management strategies for schizophrenia, offering valuable insights into improving the quality of life of patients. Future research should focus on expanding data sources through multimodal data integration, exploring deep learning models, and integrating AI-driven interventions into training tasks to fully capitalize on AI's potential in schizophrenia rehabilitation.