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Public Versus Academic Discourse on ChatGPT in Health Care: Mixed Methods Study.

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Summary

Public sentiment is largely positive regarding artificial intelligence (AI) large language models (LLMs) in healthcare, but concerns exist for mental health applications. This study compares public and expert views on LLMs in public health.

Keywords:
ethics, medicalhealth knowledge, attitudes, practicelarge language modelsnatural language processingsentiment analysissocial media discoursestructural topic modeling

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Area of Science:

  • Public Health
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • The emergence of AI large language models (LLMs) has sparked debate on their benefits and ethical implications.
  • Existing research lacks a clear understanding of public perception versus expert opinion on LLMs in public health.

Purpose of the Study:

  • To explore differences in public and academic expert views on LLMs, specifically OpenAI's ChatGPT.
  • To understand the future role of LLMs in healthcare settings.

Main Methods:

  • A hybrid sentiment analysis approach using R and GPT-3.5 achieved 84% accuracy.
  • Structural topic modeling identified 8 key discussion themes on LLMs.

Main Results:

  • Predominantly positive sentiment towards LLM integration in patient care and clinical decision-making.
  • Concerns identified regarding LLM suitability for mental health support and patient communication.

Conclusions:

  • LLMs hold transformative potential for public health, but ethical and practical challenges require attention.
  • Comparing public discourse with academic perspectives informs the debate on LLM adoption in healthcare.