AI-Driven Approaches to Addressing Opioid Use Disorder and Mental Health Crises

The field of mental health and substance abuse research is shifting towards leveraging artificial intelligence (AI) and natural language processing (NLP) to improve crisis intervention, public health surveillance, and harm reduction strategies. Recent studies have demonstrated the potential of large language models (LLMs) in detecting nuanced emotional cues, identifying psychological crises, and reducing stigma around opioid use disorder (OUD) in online communities. Noteworthy papers include:

  • A study that introduced a LLM-based text transfer recognition method for social network crisis intervention, which outperformed traditional models in crisis detection accuracy.
  • Research that found exposure to content written by LLMs can reduce stigma around OUD in online communities.
  • A proposed AI-driven NLP framework that achieved high accuracy in detecting drug use and overdose symptoms on social media, highlighting the potential of AI for supporting public health surveillance and personalized intervention strategies.

Sources

Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method

Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit

Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities

Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media

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