Should I and Can I Use AI for Forensic Psychiatry Report Writing?

Area of Science:

• Forensic Psychiatry
• Artificial Intelligence (AI)
• Large Language Models (LLMs)

Background:

• AI, particularly LLMs, shows promise in medical documentation and information management.
• Forensic psychiatric reports require transparent reasoning, evidence weighing, and accountability, differing from routine clinical notes.
• The integration of AI into legal evidence preparation necessitates careful consideration of its capabilities and limitations.

Purpose of the Study:

• To examine the potential and appropriateness of using AI in forensic psychiatry report writing.
• To integrate empirical evidence, forensic guidance, and legal frameworks to assess AI's role.
• To propose a defensible model for AI implementation in this specialized field.

Main Methods:

• Review of recent empirical evidence on LLM performance in medical and documentation tasks.
• Analysis of forensic psychiatry guidance, legal/regulatory frameworks, and AI governance recommendations.
• Comparative analysis of human expert fallibility versus AI vulnerabilities (e.g., biases vs. hallucinations).

Main Results:

• Current AI is suitable for bounded, verifiable tasks like document organization, transcription, and summarization.
• AI exhibits vulnerabilities such as hallucinations and limited explainability, posing risks to expert evidence reliability.
• Insufficient evidence supports AI in generating psycholegal reasoning, credibility assessments, or final forensic opinions.

Conclusions:

• AI can serve as a supervised productivity tool for administrative aspects of forensic reports ('AI around the report').
• Core elements of forensic reporting—interpretation, accountability, and reasoning—remain human responsibilities.
• Forensic psychiatrists must retain full authorship and accountability for all substantive conclusions, ensuring judicial scrutiny.