Literature Review: Artificial Intelligence in Public Health: Opportunities, Challenges, and Policy Implications

Artificial intelligence (AI) has emerged as a transformative technology with substantial potential to improve public health systems and research. AI technologies are capable of rapidly processing extensive and complex datasets, generating predictive insights, supporting evidence-based decision making, and increasing efficiency in tasks involving text, image, and data analysis. In public health, AI applications include disease surveillance, epidemiological modeling, health communication, resource allocation, and automation of administrative processes. Despite these promising advantages, the implementation of AI in public health is accompanied by important ethical, technical, and regulatory concerns. Major challenges include algorithmic bias, inequitable data representation, threats to privacy and cybersecurity, inadequate digital infrastructures, and insufficient workforce preparedness. Furthermore, excessive reliance on AI systems may weaken critical human judgment and create accountability issues. Policy makers and public health institutions therefore play a critical role in ensuring that AI technologies are implemented responsibly and ethically. Strong regulatory frameworks, transparent governance systems, workforce training, equitable data practices, and cybersecurity measures are essential to maximize the benefits of AI while minimizing its risks. This review discusses the major opportunities and challenges of AI in public health and highlights policy considerations necessary for sustainable and equitable implementation.