Integrating Neurotechnology and AI for Psychiatric Aggression Management: A Paradigm Shift in Clinical and Educational Frameworks

Abstract

Aggression in psychiatric patients presents a significant challenge in mental healthcare, necessitating an interdisciplinary approach that integrates neuroscience, artificial intelligence (AI), and ethical considerations. Traditional management strategies often rely on restrictive interventions, raising ethical concerns regarding patient autonomy and therapeutic efficacy. Recent advancements in neuroimaging and AI-driven behavioural analytics offer the potential to transition from reactive to predictive and preventative approaches. However, while these innovations hold promise for individualised interventions and real-time risk assessment, they also introduce critical ethical concerns, including algorithmic bias, data privacy, misclassification risks, and patient autonomy violations. This study critically examines the neurobiological underpinnings of aggression, focusing on dysfunctions in the prefrontal cortex, amygdala hyperactivity, neurotransmitter imbalances, and neuroinflammation, while evaluating AI-based predictive models in psychiatric aggression management. Additionally, the ethical and legal frameworks surrounding AI in psychiatry are discussed, with a focus on bias-mitigation strategies, transparency in AI decision-making, and informed consent challenges. The study also explores practical pathways for integrating AI into psychiatric care workflows, outlining future directions for clinical research and policy development. By merging neuroscientific insights, AI innovations, and ethical frameworks, this study advocates for a patient-centered, technology-assisted aggression management approach that balances predictive analytics with human oversight and ethical accountability.