Artificial intelligence in forensic mental health: A review of applications and implications

Abstract

This narrative review explores the transformative role of artificial intelligence (AI) in forensic mental health, focusing on its applications, benefits, limitations, and ethical considerations. AI's capabilities, particularly in areas such as risk assessment, mental health screening, behavioral analysis, and treatment recommendations, present promising advancements for accuracy, efficiency, and objectivity in forensic evaluations. Predictive models and natural language processing enhance the precision of high-stakes assessments, enabling early intervention and optimized resource allocation. However, AI's integration in forensic mental health also brings significant challenges, particularly regarding data quality, algorithmic bias, transparency, and legal accountability. Limited access to high-quality, representative data can hinder reliability, while biases within AI models risk perpetuating existing disparities. Ethical concerns surrounding data privacy and the "black box" nature of many AI algorithms underscore the need for transparency and accountability. The review highlights future directions for responsible AI use, including improving data standards, fostering interdisciplinary collaboration, and establishing robust regulatory frameworks to safeguard ethical and fair AI applications in forensic settings. Balancing technological innovation with ethical considerations and legal obligations is essential to ensure AI supports justice and upholds public trust. This review calls for ongoing research, policy development, and cautious implementation to harness AI's potential while protecting individuals' rights within the justice system.

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