Health Surveillance and AI: Balancing Public Safety and Community Privacy

Abstract

The integration of artificial intelligence (AI) into health surveillance systems has revolutionized the ability to monitor public health, predict outbreaks, and optimize resource allocation. However, this technological advancement raises significant concerns regarding public safety and community privacy. While AI enhances the efficiency of surveillance by analyzing large datasets to identify patterns and trends, its widespread use can inadvertently infringe on individual privacy rights. This paper explores the delicate balance between leveraging AI for public safety and maintaining the confidentiality of personal health data. The use of AI in health surveillance includes monitoring infectious diseases, tracking vaccination rates, and predicting health trends, all of which require vast amounts of sensitive data. As a result, there is a growing need for robust privacy frameworks and ethical guidelines to govern the use of AI in health surveillance. This paper also examines case studies where AI implementation has either safeguarded or violated privacy, with an emphasis on data anonymization, informed consent, and the role of regulatory bodies. The research highlights the importance of transparent policies that protect citizens’ privacy while allowing AI to contribute to public health goals. Ultimately, the challenge lies in ensuring that AI technologies are deployed in a manner that prioritizes both the security of public health and the protection of individual privacy rights.