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Large Language Models in Mental Health Research and Treatment
Steven Mesquiti1, Erik C Nook1
1Department of Psychology, Princeton University, Princeton, New Jersey, USA; email: sm9518@princeton.edu, enook@princeton.edu.
Large language models (LLMs) show promise for improving mental health assessment and care accessibility. However, significant challenges like bias and safety risks must be addressed for responsible AI implementation.
Area of Science:
- Psychiatry
- Computer Science
- Digital Health
Background:
- Mental health conditions represent a significant global health burden.
- Traditional psychological assessments are resource-intensive and lack accessibility.
- Artificial intelligence (AI), especially large language models (LLMs), offers potential solutions.
Purpose of the Study:
- To review the current applications of LLMs in mental health.
- To identify the potential benefits and challenges of using LLMs in mental healthcare.
- To outline future directions for safe and equitable LLM integration.
Main Methods:
- Literature review synthesizing recent applications of LLMs in mental health.
- Analysis of LLM use in psychopathology assessment, digital phenotyping, and electronic health records.
- Examination of early integrations of LLMs into psychotherapy.
Main Results:
- LLMs are being explored for language-based assessment and digital phenotyping.
- Applications include analyzing electronic health records and aiding psychotherapy.
- Significant risks identified: bias, hallucinations, and unsafe recommendations.
Conclusions:
- LLMs present opportunities to enhance mental health assessment and intervention.
- Ethical considerations and regulatory oversight are crucial for safe deployment.
- Future research must prioritize equitable and responsible AI in mental healthcare.
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