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Leveraging ChatGPT to optimize depression intervention through explainable deep learning.

Yang Liu1,2, Xingchen Ding3, Shun Peng4

  • 1School of Information Management, Wuhan University, Wuhan, China.

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|June 21, 2024
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Summary

Large language models like ChatGPT show promise in mental health interventions, achieving 93.76% accuracy. This AI-generated content (AIGC) can complement human counseling effectively.

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AIGCChatGPTHGCdepression interventionexplainable deep learning

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Mental Health Technology
  • Computational Linguistics

Background:

  • Mental health issues pose a significant global burden.
  • Large language models (LLMs) like ChatGPT demonstrate potential for depression intervention.
  • Evaluating AI's role alongside human counselors is crucial.

Purpose of the Study:

  • To assess ChatGPT's viability as a tool for counselors.
  • To compare AI-generated content (AIGC) with human-generated content (HGC).
  • To explore AI's role in enhancing mental health interventions.

Main Methods:

  • A novel framework integrating ChatGPT, BERT, and SHAP was proposed.
  • ChatGPT generated responses, BERT classified content reliability, and SHAP provided interpretability.
  • The methodology aimed to improve accuracy and effectiveness in mental health support.

Main Results:

  • The proposed methodology achieved a high accuracy rate of 93.76%.
  • ChatGPT responses were consistently polite, considerate, and used accessible language.
  • AI-generated content (AIGC) was found to be a valuable complement to traditional interventions.

Conclusions:

  • LLMs offer significant promise for advancing healthcare and patient care.
  • AI-generated content (AIGC) can augment existing counseling practices.
  • This study marks a step towards sophisticated AI-driven healthcare systems.