Using Large Language Models in Public Health Platforms: A Study of Platform Users’ and Editors’ Perspectives on Trust, Ethics and Risks

Abstract

In the Netherlands, several public health platforms provide information and peer support to different patient groups such as people suffering from cancer or multiple sclerosis. With the rapid growth and development of Large Language Models (LLMs) in multiple sectors, these health platforms are considering the use of LLMs for personalized content generation and ease of information management. However, the sensitive nature of health information and personal experiences shared on such platforms introduces privacy and ethical risks, such as misinformation and bias. This study explores the concerns and risks associated with LLMs through interviews and focus groups with platform editors and users who use and access these platforms. Our findings show that risks related to content quality and quantity were most frequently identified. Moreover, the findings highlight the importance of disclosure if there is no human oversight, participants’ strong opposition to AI-generated blogs, and the potential of LLMs for personalization, provided users retain control over what they read. This work contributes to the ongoing discussion in human-centered computing about the ethical challenges and risks of adopting LLMs by presenting an empirical evaluation with editors and users. Moreover, the insights inform considerations and design guidelines for implementing LLMs on public health platforms.

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