Schizophrenia spectrum disorders (SSDs) are chronic conditions associated with profound functional impairment, disability and economic burden. Traditional approaches to diagnosis and management often struggle to capture the heterogeneity of SSD. In recent years, machine learning (ML) has expanded rapidly in mental health research, offering ways to analyse complex datasets, uncover subtle patterns and generate predictive models that could inform more individualised interventions.
This scoping review aimed to map how ML has been applied to SSD research with emphasis on clinical and functional outcome trajectories.
Peer-reviewed studies investigating any use (eg, predictive tool, treatment algorithm) of ML in determining outcomes in SSD were eligible. Non-English language studies, grey literature, systematic reviews and conference abstracts were excluded.
Following Arksey and O’Malley’s framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews, we searched three bibliographic databases (MEDLINE, APA PsycInfo, Embase) with input from a health sciences librarian. The initial search was completed on 28 July 2023 and re-run on 10 March 2025.
Two independent reviewers screened studies at title/abstract and full-text stages and extracted data using a standardised table in Covidence, capturing study design, ML techniques, data sources and outcomes. Methodological and reporting quality of included studies was appraised using the APPRAISE-AI instrument.
88 studies met inclusion criteria. For included studies, n=62 were cross-sectional, n=19 were cohort studies, n=5 were case-controls, n=1 was an evaluation study and n=1 carried out a secondary analysis of data from a randomised controlled trial. Overall, methodological and reporting quality were moderate (mean APPRAISE-AI score 47.4/100, range 31–65). ML applications were grouped into five domains: clinical, treatment, functional, behavioural and provider outcomes. Clinical outcome studies used electronic health records, clinical assessments, neuroimaging and mobile sensing to predict relapse, remission, symptom severity and suicide risk. Treatment studies modelled treatment response, resistance and adverse effects, with attention to clozapine and electroconvulsive therapy. Functional studies employed deep learning to analyse speech, social cognition and real-world functioning. Behavioural outcomes included prediction of aggression, self-harm and offending. Provider outcomes examined prescribing patterns using supervised learning. Across domains, integration of multimodal data and adoption of advanced approaches such as deep neural networks were evident.
ML in SSD research shows significant promise for advancing diagnosis, prognosis and treatment planning. However, methodological rigour, interpretability, fairness and clinical validation remain critical for future translation into practice.