This work explores the transformative role of predictive medicine and artificial intelligence in addressing the structural challenges of rare diseases within the Brazilian public health system (SUS). It highlights how fragmented data, delayed diagnoses, and limited access to specialized care create significant barriers for millions of patients. Building on a data-driven approach, the study proposes the integration of digital health platforms, predictive models, and automation to enhance clinical surveillance, support decision-making, and enable personalized care. By combining statistical methods with AI, the research demonstrates that while traditional models provide robust quantitative results, artificial intelligence adds a critical layer of interpretability, clinical guidance, and actionable insights. The study also emphasizes the importance of governance, data protection (LGPD compliance), interoperability, and professional training to ensure responsible adoption. Ultimately, it positions predictive medicine not only as a technological advancement but as a strategic pathway to equity, efficiency, and systemic strengthening in healthcare.
Paula Lopes Alvim Santilli (Sun,) studied this question.