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Updated: Jun 27, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large Language Models for Depression Detection: A Review with Prospects of Incomplete Multimodality.

Anqi Dai1, Weipeng Shi1, Xiaogang Gu1

  • 1School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China.

Brain Sciences
|June 26, 2026
PubMed
Summary

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Accurate depression detection is crucial due to rising global rates. This review explores current methods, focusing on large language models (LLMs) for improved depression recognition, especially with incomplete data.

Area of Science:

  • Psychiatry and Mental Health
  • Artificial Intelligence in Healthcare
  • Computational Linguistics

Background:

  • Depression is a leading cause of global disability, with increasing prevalence.
  • Accurate and efficient depression detection remains a significant challenge.
  • Existing research covers epidemiological status, assessment scales, and datasets.

Purpose of the Study:

  • To provide a comprehensive review of depression recognition research.
  • To focus on large language model (LLM)-based methods for depression detection.
  • To address the challenge of incomplete multimodal data in depression recognition.

Main Methods:

  • Review of unimodal analysis and multimodal fusion techniques for depression recognition.
  • Emphasis on large language model (LLM) applications in depression detection.
Keywords:
affective computingdeep learningdepression detectionincomplete multimodal learninglarge language models

Related Experiment Videos

Last Updated: Jun 27, 2026

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

Published on: December 6, 2024

  • Identification of research gaps, particularly in incomplete modality scenarios.

    • Main Results:

    • LLMs show potential in handling incomplete multimodal data for depression recognition.
    • A critical gap exists in real-world, incomplete modality depression recognition.
    • Future directions focus on LLM-driven, clinically applicable solutions.

    Conclusions:

    • LLM-based approaches offer promising avenues for advancing depression recognition.
    • Addressing incomplete data is key for clinical applicability.
    • Ethical considerations and human-centered deployment are vital for LLM systems in healthcare.