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Updated: May 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Large Language Models and Their Applications in Mental Health: Scoping Review.

Matheus Calvin Lokadjaja¹, Jordon Junyang Kho¹, Peter Johannes Schulz^1,2,3

¹Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

JMIR Mental Health

|May 15, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) show promise in mental health diagnostics and support. However, the field is nascent, requiring robust validation with real clinical data and standardized protocols for safe integration.

Keywords:

artificial intelligence generative AI large language models mental health natural language processing

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Area of Science:

Artificial Intelligence in Healthcare
Computational Psychiatry
Digital Mental Health

Background:

Large language models (LLMs) are increasingly recognized for their potential to revolutionize mental health care through advanced diagnostic, prognostic, and decision support capabilities.
The proliferation of LLMs in scientific literature highlights a growing interest in their clinical applications, necessitating a review of the current landscape.
Understanding the diverse models, optimization strategies, and use cases is crucial for identifying future research and clinical impact areas.

Conclusions:

The use of LLMs in mental health is rapidly expanding but remains in an exploratory phase.
Future research must prioritize standardized model adaptation techniques to ensure patient safety and seamless integration into clinical workflows.
Rigorous evaluation using standardized protocols and real clinical outcome measures is essential for validating LLM efficacy and reliability in mental health.