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Summary
This summary is machine-generated.

Large language models (LLMs) present risks in public health. This study introduces a risk taxonomy to help professionals evaluate LLM adoption and mitigate potential harms in diverse health contexts.

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generative AIhealth informational needinformation and communication technologyinformation seeking and supportlarge language modelspublic healthrisk taxonomy

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Area of Science:

  • Public Health
  • Artificial Intelligence
  • Health Communication

Background:

  • Large language models (LLMs) offer potential but raise concerns for public health information dissemination.
  • Existing risk assessment frameworks for LLMs are insufficient for the high-stakes public health domain.

Purpose of the Study:

  • To explore concerns regarding LLM adoption in public health.
  • To develop a risk taxonomy for evaluating LLM use in critical public health areas.

Main Methods:

  • Conducted focus groups with public health professionals and individuals with lived experience.
  • Examined concerns across infectious disease prevention, opioid use disorder, and intimate partner violence.

Main Results:

  • Developed a risk taxonomy with four dimensions: individual, human-centered care, information ecosystems, and technology accountability.
  • Identified specific risks and provided reflection questions for practitioners.

Conclusions:

  • LLM adoption in public health requires a risk-reflexive approach.
  • A shared vocabulary and reflection tool are needed for collaborative risk assessment and harm mitigation.
  • Emphasizes the importance of lived experience and domain expertise in evaluating LLM applications.