Contextualizing Clinical Benchmarks: A Tripartite Approach to Evaluating LLM-Based Tools in Mental Health Settings

Area of Science:

• Clinical Informatics
• Artificial Intelligence in Healthcare
• Mental Health Technology

Background:

• The widespread adoption of Large Language Models (LLMs) in mental health necessitates robust clinical evaluation.
• Existing evaluation methods for Artificial Intelligence (AI) tools are often theoretical and not immediately applicable to clinical settings.
• There is a critical need for practical, implementable frameworks to assess LLM safety and efficacy in mental health care.

Purpose of the Study:

• To introduce a practical, tripartite evaluation framework for LLM-based tools in mental health.
• To provide actionable assessment methods for immediate clinical implementation.
• To establish a foundation for developing specialized benchmarks for AI in mental health.

Main Methods:

• A three-layered framework: technical profile, health care knowledge, and clinical reasoning.
• Assessment methods include direct model questioning and adversarial testing.
• Focus on immediate implementability for clinical teams.

Main Results:

• The proposed framework offers immediate practical guidance for evaluating LLM tools.
• It addresses foundational safety, domain-specific knowledge, and clinical reasoning capabilities.
• The approach facilitates the development of essential benchmarks for AI in mental health.

Conclusions:

• The tripartite framework provides an actionable approach to evaluating LLM tools in mental health care.
• Immediate implementation of these methods can enhance patient care and safety.
• This framework supports the collective development of specialized AI evaluation resources for mental health.