The role of artificial intelligence in detecting avian influenza virus outbreaks: A review.

Abstract

Avian influenza remains a significant threat to the global poultry industry and public health, necessitating rapid and accurate diagnostic methods. Traditional diagnostic techniques, such as serological assays and polymerase chain reaction-based methods, have proven effective, but they often lack the speed and predictive capability required for early intervention. The integration of artificial intelligence (AI) has revolutionized avian influenza detection by using machine learning models for early disease prediction and AI-driven imaging for accurate diagnosis. Additionally, AI-enhanced molecular diagnostic techniques and biosensors significantly increase the sensitivity and specificity of detecting poultry diseases. The combination of big data analytics and AI enables real-time monitoring, which improves forecasting of outbreaks and response strategies. By integrating data from various sources, such as genetic, environmental, and epidemiological information, AI enhances the early detection and risk assessment of diseases. Additionally, AI models are becoming essential for predicting how diseases might spread from animals to humans, which helps prevent infections. However, challenges such as data biases, ethical concerns, and the need for standardized protocols must be addressed to ensure responsible AI deployment. As technology progresses, AI is poised to revolutionize the management of avian influenza, providing a proactive and data-informed method for controlling diseases, ultimately protecting the health status.