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Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Mental Health Technology

Background:

  • Large language model (LLM) chatbots are increasingly utilized for behavioral health support.
  • Rigorous evaluations of their advice on alcohol misuse are limited.

Purpose of the Study:

  • To evaluate the performance of publicly available chatbots in responding to alcohol misuse scenarios.
  • To assess chatbot interactions across domains including empathy, information quality, and usefulness.

Main Methods:

  • Seven chatbots (general and behavioral health-focused) were tested using simulated longitudinal interactions over seven days.
  • Clinicians rated transcripts based on empathy, information quality, usefulness, responsiveness, and scope awareness.
  • Secondary assessments included stigmatizing language and validation versus challenging the user.

Main Results:

  • Empathy was the highest-rated domain (mean score 4.6/5), while quality of information was the lowest (mean 2.7/5).
  • Performance varied significantly across chatbots, with no difference between general and behavioral health types.
  • All chatbots provided some inappropriate, overstated, or inaccurate guidance, despite avoiding stigmatizing language.

Conclusions:

  • Chatbots demonstrate strengths in empathy but require significant improvement in the quality and accuracy of information provided for alcohol misuse support.
  • Users and clinicians should be aware of current chatbot limitations in this domain.
  • Further development is needed to enhance the reliability of AI-driven behavioral health tools.