AI-Driven Predictive Analytics for Syndromic Surveillance: Enhancing Early Detection of Emerging Infectious Diseases in the United States
Year: 2025
Type: article
Abstract: Emerging infectious diseases are a major concern to public health in the United States, requiring advanced surveillance technologies for early diagnosis and response. The incorporation of artificial intelligence (AI)-driven predictive analytics into syndromic surveillance represents a game-changing technique that uses big data, machine learning, and real-time health indicators to improve disease outbreak detection. The purpose of this review is to explore AI-driven predictive analytics in syndromic surveillance, emphasizing its ability to increase early detection of emerging infectious diseases in the United States. The findings indicate that AI-driven predictive analytics increases the speed, accuracy, and scalability of syndromic surveillance. AI-powered methods, such as deep learning and natural language processing, may identify anomalies in symptom patterns, monitor disease progression, and predict epidemics more accurately. However, with the proper safety measures in place, AI has the potential to transform public health surveillance, increasing likely national preparedness for emerging infectious disease threats.
Author: Chinedu Osita Agbakwuru
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4409780123