Large Language Models and Mental Health: a Safety Evaluation in Depression Treatment

EasyChair Preprint no. 12267

Abstract

Large language models (LLMs), such as OpenAI's GPT-3.5, have gained significant attention for their potential applications in various fields, including mental health care. In this study, we conduct a safety evaluation of using LLMs in depression treatment. Depression is a widespread mental health disorder characterized by persistent sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, low energy, and poor concentration. While traditional treatments such as therapy and medication are effective for many individuals, access to these resources can be limited due to various factors, including cost, stigma, and availability of mental health professionals. LLMs present an opportunity to address some of these barriers by providing accessible, scalable, and potentially personalized support for individuals experiencing depression. However, concerns have been raised regarding the safety of deploying LLMs in mental health contexts, including the potential for reinforcement of negative thought patterns, misinformation, and breaches of privacy. Through a comprehensive review of the literature and consultation with mental health professionals, we assess the potential risks and benefits of integrating LLMs into depression treatment. Our findings highlight the importance of careful design, rigorous evaluation, and ongoing monitoring to mitigate risks and maximize the therapeutic potential of LLMs in supporting individuals with depression.

Keyphrases: Accessibility, Depression treatment, large language models, medication, mental health, misinformation, negative thought patterns, Personalized support, Privacy, Risk Mitigation, safety evaluation, Scalability, Therapeutic potential, therapy

@Booklet{EasyChair:12267,
  author = {Usman Hider},
  title = {Large Language Models and Mental Health: a Safety Evaluation in Depression Treatment},
  howpublished = {EasyChair Preprint no. 12267},

  year = {EasyChair, 2024}}