Ethical, regulatory and clinical challenges of implementing explainable artificial intelligence-driven structural magnetic resonance imaging violence prediction in schizophrenia care

Aggressive and violent behavior creates heavy clinical burdens in schizophrenia care.Conventional risk-assessment approaches are subjective and show only limited predictive performance.Structural magnetic resonance imaging-driven explainable artificial intelligence (AI) models offer potential neuroanatomical biomarkers for investigational risk-support approaches for violence-risk stratification.Still, nontechnical barriers blocking real-world clinical translation have received little research attention.This narrative review critically analyzes ethical, regulatory, and clinical obstacles to deploying such tools.Key ethical concerns cover privacy risks of sensitive neuroimaging datasets, cross-population algorithmic bias, barriers to valid informed consent, and risk of amplified social stigma.Major regulatory shortcomings consist of missing standardized validation benchmarks, unresolved medicolegal liability, and inconsistent cross-border data-governance rules.Clinical hurdles comprise weak model generalizability, misalignment between theoretical explainability and practical usability, limited digital literacy among clinicians, and poor compatibility with established care workflows.Addressing these obstacles demands cross-sector collaboration.Clinicians, AI engineers, ethicists, regulators, and patients should jointly build customized ethical and regulatory frameworks.These frameworks can support responsible, fair, and clinically useful implementation, and provide practical guidance for future neuro-AI mental-health policy formulation and clinical decision-making.