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A Scoping Review of Large Language Model Chatbot Use for Alcohol and Other Drug Health Information
Natasha Harding1, Nataly Bovopoulos1, Dotahn Caspi1
1Alcohol and Drug Foundation, Melbourne, Australia.
Large Language Model (LLM) chatbots show promise for alcohol and drug information seeking, but current research highlights significant challenges in user safety, accuracy, and adherence to guidelines. Further investigation is crucial for safe and effective use.
Area of Science:
- Digital Health
- Health Informatics
- Artificial Intelligence in Healthcare
Background:
- Online alcohol and drug information (AOD) seeking faces quality and accessibility challenges.
- Large Language Model (LLM) chatbots offer a potential solution to these barriers.
Purpose of the Study:
- To review the literature on chatbot use for seeking AOD health information.
- To identify benefits, challenges, and recommendations for future LLM chatbot applications in AOD information seeking.
Main Methods:
- A scoping review methodology was employed.
- Systematic search of four databases for English language studies in the last five years.
- Screening of 243 articles, with five included studies.
Main Results:
- Growing interest but limited evidence on LLM chatbots for AOD information.
- Benefits include accuracy and user experience; challenges involve user safety, referral, quality, and guideline adherence.
- Mixed results on evidence-based responses; only three studies recommended chatbots.
Conclusions:
- Knowledge gaps exist regarding LLM chatbot accuracy, user safety, readability, evidence base, and quality for AOD queries.
- More research is needed to assess the safe and non-stigmatizing application of LLMs for obtaining AOD information.
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