Predictive utility of artificial intelligence on schizophrenia treatment outcomes: A systematic review and meta-analysis

Abstract

Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligence (AI) shows promise in predicting outcomes, prompting this systematic review and meta-analysis to evaluate various AI models' predictive utilities in schizophrenia treatment. A systematic search was conducted, and the risk of bias was evaluated. The pooled sensitivity, specificity, and diagnostic odds ratio with 95 % confidence intervals between AI models and the reference standard for response to treatment were assessed. Diagnostic accuracy measures were calculated, and subgroup analysis was performed based on the input data of AI models. Out of the 21 included studies, AI models achieved a pooled sensitivity of 70 % and specificity of 76 % in predicting schizophrenia treatment response with substantial predictive capacity and a near-to-high level of test accuracy. Subgroup analysis revealed EEG-based models to have the highest sensitivity (89 %) and specificity (94 %), followed by imaging-based models (76 % and 80 %, respectively). However, significant heterogeneity was observed across studies in treatment response definitions, participant characteristics, and therapeutic interventions. Despite methodological variations and small sample sizes in some modalities, this study underscores AI's predictive utility in schizophrenia treatment, offering insights for tailored approaches, improving adherence, and reducing relapse risk.