Abstract

Avian influenza remains a significant threat to global poultry industries and public health necessitating rapid and accurate diagnostic methods. The traditional diagnostic techniques, such as serological assays and PCR-based methods, have proven effective, but often lack the speed and predictive capability required for early intervention. The integration of artificial intelligence (AI) has revolutionized avian influenza detection, utilizing machine learning models for early disease prediction and AI-driven imaging for accurate diagnosis. Additionally, AI-enhanced molecular diagnostic techniques and biosensors significantly boost sensitivity and specificity in detecting poultry diseases. The combination of big data analytics and AI enables real-time monitoring, which improves the forecasting of outbreaks and the strategies for responding to them. By integrating data from various sources such as genetic, environmental and epidemiological information, AI enhances the early detection and the assessment of risks of diseases. Additionally, AI models are becoming essential in predicting how diseases might spread from animals to humans, which helps in preventing 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 health status.

Key words: Avian influenza, Artificial intelligence, Zoonotic transmission, Diagnosis, Biosensors, Surveillance, Early detection