Artificial intelligence in Brazilian public health: potential, challenges, and ethical implications for the Brazilian Unified Health System

Abstract

Objectives: To critically analyze the potential, challenges, and ethical implications of incorporating artificial intelligence (AI) into Brazilian public health, in light of the principles of the Unified Health System (SUS), also considering its interfaces with epidemiological practice. Methods: This is a theoretical-analytical paper based on national and international literature, which articulates core AI concepts with political-epistemological reflections from Public Health. The approach includes discussions on machine learning, deep learning, natural language processing, and large language models, focusing on applications within the SUS context. Results: Multiple opportunities for using AI to strengthen the SUS are identified, including prediction of health events, diagnostic support, service regulation, and public policy development. However, structural barriers such as fragmented information systems, regional inequalities, and gaps in professional training are highlighted. Issues such as algorithmic fairness, explainability, technological sovereignty, and digital literacy emerge as key dimensions for the responsible adoption of AI. Conclusion: AI is not neutral, and its integration into the SUS must be guided by democratic principles and sensitivity to social vulnerabilities, or it risks reinforcing technocratic and exclusionary models. The struggle over the meaning of innovation is, therefore, also a struggle over the future of public health in Brazil.