THE USE OF ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF LABORATORY SUPPLIES AND THE PREDICTABILITY OF DISEASES IN SEASONAL PERIODS

Abstract

Seasonal diseases such as influenza, dengue, and RSV represent significant challenges for the SUS (Brazilian Unified Health System), generating substantial economic impacts. This study analyzed the application of artificial intelligence in the predictability of these diseases and laboratory supply management. Twenty-nine studies were identified, of which 14 met the inclusion criteria. The results demonstrate consistent use of AI for epidemiological prediction, especially LSTM models for temporal analysis. However, there is a gap in the integration between predictive models and operational management of laboratory supplies in Brazil. This absence represents an opportunity to develop solutions that optimize resources and reduce costs in the Brazilian health system