Schizophrenia in the age of artificial intelligence: A review of advances in diagnosis, prediction, and digital psychiatry

Abstract

Schizophrenia is a chronic and disabling psychiatric disorder affecting approximately one percent of the world’s population. It manifests through positive, negative, and cognitive symptoms that severely impair social and occupational functioning. Despite extensive research, diagnosis remains primarily subjective, based on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria, and effective early intervention is still limited. This narrative review synthesizes current evidence from epidemiological, neurobiological, and clinical studies alongside recent advances integrating artificial intelligence (AI) into schizophrenia research. Literature sources were drawn from PubMed, Scopus, and Web of Science, focusing on studies addressing etiology, neuroimaging findings, treatment outcomes, and AI-based diagnostic approaches. The etiology of schizophrenia is multifactorial, involving genetic vulnerability, neurodevelopmental disturbances, neurotransmitter dysregulation, and environmental stressors such as perinatal complications and substance use. Neuroimaging findings consistently reveal gray matter reduction, ventricular enlargement, and prefrontal-temporal connectivity abnormalities. Pharmacological management especially with second-generation antipsychotics such as clozapine, risperidone, and olanzapine remains the treatment cornerstone, supported by psychosocial interventions that improve adherence and functional recovery. Emerging AI-driven tools using neuroimaging, electroencephalography, and behavioral data show high diagnostic accuracy and potential for personalized treatment planning. Schizophrenia continues to present diagnostic and therapeutic challenges due to its biological complexity and clinical heterogeneity. The review stands out because it uses modern AI methods to study schizophrenia for better diagnosis and treatment planning and earlier disorder identification. The review combines traditional medical methods with new computational systems to demonstrate schizophrenia research progress while developing vital ethical standards and procedural systems for psychiatric care during the AI age.

Keywords: Schizophrenia; Artificial intelligence; Explainable artificial intelligence; Electroencephalography; Magnetic resonance imaging; Neuroimaging; Speech analysis; Digital psychiatry; Machine learning; Precision psychiatry

Core Tip: Schizophrenia remains one of the most complex psychiatric disorders, characterized by profound neurobiological, cognitive, and social dysfunctions. Despite decades of clinical and neuroimaging research, diagnosis still depends largely on subjective observation. This review integrates classical perspectives on epidemiology, etiology, and treatment with recent developments in artificial intelligence (AI). It highlights how AI-driven models-particularly those analyzing neuroimaging and electroencephalography data-offer objective insights into brain alterations and symptom mechanisms. By bridging traditional psychiatry with emerging computational tools, the paper outlines a roadmap for early detection, personalized intervention, and transparent decision support in schizophrenia care.