Explainable artificial intelligence (XAI) in schizophrenia: enhancing transparency in clinical decision-making.
Authors
Affiliations (2)
Affiliations (2)
- Department of Psychiatry, Ganesh Shankar Vidyarthi Memorial (GSVM) Medical College, Kanpur, Uttar Pradesh, India.
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, India.
Abstract
Schizophrenia is a chronic and severe psychiatric disorder affecting nearly 1% of the global population, characterized by positive symptoms such as hallucinations and delusions, negative symptoms including avolition and affective flattening, and cognitive impairments that compromise social and occupational functioning. Despite advances in psychiatry, diagnosis remains largely symptom-based, relying on criteria from the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Classification of Diseases (ICD-11), highlighting the need for objective biomarkers and improved clinical decision support. Neuroimaging modalities, including structural and functional magnetic resonance imaging (MRI/fMRI), diffusion tensor imaging (DTI), positron emission tomography (PET), as well as electrophysiological techniques such as electroencephalography (EEG) and magnetoencephalography (MEG), have revealed abnormalities in brain connectivity, dopaminergic signaling, sensory processing, and network dysfunction associated with schizophrenia. Recent advances in artificial intelligence (AI), particularly machine learning (ML), have enabled prediction of diagnosis, treatment response, and relapse risk using multimodal clinical, neuroimaging, and genetic data. However, the "black-box" nature of many AI models limits transparency, interpretability, and clinician trust, restricting translation into practice. Explainable artificial intelligence (XAI) addresses these challenges by providing interpretable, transparent, and clinically accountable models that support decision-making while preserving ethical and regulatory standards. By integrating explainable models with multimodal data, clinicians can use them as an adjunct for precision psychiatry, facilitating biomarker discovery, personalized treatment strategies, and trustworthy, actionable decisions, ultimately enhancing transparency and improving outcomes in schizophrenia care.