An overview of diagnostics and therapeutics using large language models

Area of Science:

• Computational psychiatry
• Artificial intelligence in mental health

Background:

• Acute need for stress and trauma treatment solutions.
• Shortages of qualified human mental health professionals.
• Potential of AI to provide advanced screening, diagnosis, and interventions.

Purpose of the Study:

• Overview of state-of-the-art large language model (LLM) applications in mental healthcare.
• Discussion of research directions and challenges for clinical LLM deployment.
• Highlighting the need for equitable access and privacy-preserving training.

Main Methods:

• Review of current LLM applications in diagnostic assessments.
• Analysis of LLM use in clinical note generation.
• Exploration of LLM capabilities in therapeutic support.

Main Results:

• LLMs show promise in diagnostic assessments, clinical documentation, and therapeutic interventions.
• Identified open research areas and challenges for clinical LLM integration.
• Emphasized the importance of representation and privacy in AI for mental health.

Conclusions:

• LLMs present a significant opportunity to augment mental healthcare services.
• Addressing disparities and ensuring data privacy are critical for responsible AI implementation.
• Continued research and development are essential to realize the full potential of LLMs in clinical settings.