Application of Artificial Intelligence in the Prevention and Diagnosis of Avian Influenza: A Literature Review

Abstract

Avian Influenza is an important zoonotic viral disease affecting poultry and wild birds. Current prevention and control strategies are often ineffective, leading to significant economic losses and public health risks. This review highlights the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing surveillance, early detection, and prediction of avian influenza infections in poultry. Various AI and ML techniques, including Gradient-boosted trees, Convolutional Neural Networks, and Sensor-Based Detection methods, have been applied to classify the pathogenicity of avian influenza virus strains, identify sick and deceased birds, and predict the likelihood of isolating avian influenza viruses in wild bird samples. These innovative solutions can offer high accuracy and efficiency in disease detection, reducing production expenses and enhancing animal welfare. Integrating AI and ML in poultry farming can improve disease management strategies, reduce zoonotic transmission risks, and safeguard global food security. This review provides insights into the current state of AI and ML applications in avian influenza detection and surveillance, highlighting their potential to transform the poultry industry toward a more efficient, sustainable, and healthier future.