Artificial intelligence in forensic psychiatry: potential applications and key considerations.

Area of Science:

• Forensic Psychiatry
• Artificial Intelligence
• Mental Health

Background:

• Artificial intelligence (AI) presents transformative potential across societal domains.
• Forensic psychiatry requires careful evaluation of AI's utility and ethical implementation for its specific population.
• Existing AI research often overlooks the unique needs and complexities of forensic contexts.

Purpose of the Study:

• To review the potential applications of AI in clinical forensic psychiatry for diagnosis and risk assessment.
• To identify and analyze the practical, ethical, and legal challenges associated with AI implementation in this field.

Main Methods:

• This study employed a narrative review methodology.
• It synthesized existing research on AI in mental disorder screening, identification, and offending risk assessment within forensic contexts.

Main Results:

• AI shows early promise in mental disorder screening but has limitations and minimal forensic population focus.
• Offending risk assessment research is more developed, yet clinical utility is hampered by external validation and offense type specificity.
• Key challenges include data acquisition and representativeness, diagnostic heterogeneity, model transparency, reliability, and bias propagation.

Conclusions:

• Increased data collection and inter-agency sharing are recommended.
• External validation of AI models, enhanced clinician training, and involvement in AI development are crucial.
• Development of robust ethical and legal safeguards and regulations for AI in forensic psychiatry is essential.