AI and Machine Learning for Early Detection of Infectious Diseases in the US: Opportunities and Challenges

Artificial intelligence and machine learning are reshaping U.S. infectious-disease surveillance by rapidly integrating clinical, environmental, and open-source data to flag anomalies sooner than conventional methods. This article aims to assess how artificial intelligence (AI) and machine learning (ML) can accelerate early detection of emerging infectious diseases in the United States In case studies, real-time ML tools cut hospital-acquired infections by 40 %, and systems like BlueDot predicted major outbreaks days before official alerts, underscoring strong gains in speed and accuracy. However, biased training data, opaque “black-box” models, privacy risks, and high implementation costs still threaten equitable, trustworthy deployment. Overcoming these barriers will require rigorous data-quality standards, explainable algorithms, interdisciplinary governance, and scalable validation frameworks—advances that could extend early-warning capacity from local hospitals to global health security networks.