Ethical and Security Challenges of Large Language Models in Public Health Systems: A Survey

Abstract

The integration of large language models (LLMs) into public health systems promises significant improvements in disease surveillance, clinical communication, and health education. However, their deployment raises fundamental ethical and security risks-ranging from data privacy breaches to algorithmic bias and misinformation. This survey systematically reviews recent literature to elucidate these challenges, categorize emerging risks, and assess current evaluation and governance mechanisms. Our findings highlight the urgent need for interdisciplinary frameworks that embed ethical principles throughout the LLM lifecycle to ensure trustworthy and equitable AI integration in health care.

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