Use of Artificial Intelligence in the Surveillance of Seasonal Respiratory Infections

In the healthcare sector, Artificial Intelligence (AI) has introduced innovative approaches to monitor and manage seasonal respiratory infections, marking a transformative shift in disease surveillance. This narrative review delves into the potential of AI for tracking ailments such as the flu and respiratory syncytial virus (RSV). The study explores the role of AI in revolutionizing the detection, forecasting, and management of these illnesses, including the development of AI-centric predictive models such as Convolutional Neural Networks (CNNs), other conventional Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forest, etc. to make it possible to automatically interpret chest X-rays, wheeze sounds and CT images for quicker diagnosis and the use of natural language processing (NLP) to detect early symptoms through social media platforms. Additionally, the review touches upon many nations implementing stringent nonpharmaceutical interventions (NPIs) linked to the role of AI in public health. By automating the examination of enormous databases of clinical records and test results, AI digitalizes respiratory infection surveillance systems, enabling more rapid and accurate real-time outbreak identification. The technology has shown an increase of over 30% in early detection rates of respiratory infections as compared to conventional approaches in numerous research and real-world applications. In addition to facilitating prompt medical interventions, this improved early diagnosis also helps to reduce the total transmission rate by about 20%, eventually protecting the health of a sizable percentage of the population.