Zoonotic diseases originating in animals pose a significant threat to global public health. Recent outbreaks, such as coronavirus disease 2019 (COVID-19), have caused widespread illness, death, and socioeconomic disruptions worldwide. To cope with these diseases effectively, it is crucial to strengthen surveillance capabilities and establish rapid response systems. The aim of this review to examine the modern technologies and solutions that have the potential to enhance zoonotic disease surveillance and outbreak responses and provide valuable insights into how cutting-edge innovations could be leveraged to prevent, detect, and control emerging zoonotic disease outbreaks. Herein, we discuss advanced tools including big data analytics, artificial intelligence, the Internet of Things, geographic information systems, remote sensing, molecular diagnostics, point-of-care testing, telemedicine, digital contact tracing, and early warning systems. These technologies enable real-time monitoring, the prediction of outbreak risks, early anomaly detection, rapid diagnosis, and targeted interventions during outbreaks. When integrated through collaborative partnerships, these strategies can significantly improve the speed and effectiveness of zoonotic disease control. However, several challenges persist, particularly in resource-limited settings, such as infrastructure limitations, costs, data integration and training requirements, and ethical implementation. With strategic planning and coordinated efforts, modern technologies and solutions offer immense potential to bolster surveillance and outbreak responses, and serve as a critical resource against emerging zoonotic disease threats worldwide.

The integration of Artificial Intelligence (AI) in various sectors has led to significant advancements, with the animal industry being no exception. This review aims to investigate the benefits, limitations, and future prospects of AI technology in improving animal welfare. First, it examines the role of AI in understanding animal behaviors and emotions, providing deeper insights into their well‐being and sources of stress. Next, the paper explores how AI can revolutionize animal nutrition through innovative algorithms and data analytics. The health aspect emphasizes the ability of AI to identify and manage illnesses through intelligent systems. This review also highlights the application of AI in improving animal living conditions, with a focus on environmental management and automated cleaning and disinfection systems. In conclusion, the review emphasizes AI‐driven techniques for early prediction, close monitoring, and accurate diagnosis of animal diseases, ensuring healthier and more sustainable livestock management. By leveraging its advantages, addressing limitations, and exploring future directions, AI has the potential to significantly enhance animal welfare, sustainable agriculture, and veterinary practices.

• AI enables COVID-19 forecasting, diagnosis, decision support for response and control. • Intelligent systems aid risk analysis and policymaking to combat COVID-19. • AI accelerates drug repurposing and discovery of COVID-19. • Multidisciplinary collaboration vital responsible AI solutions against COVID-19.

Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.

In the past few decades, biosensors have been gradually developed for the rapid detection and monitoring of human diseases. Recently, functional nucleic-acid (FNA) biosensors have attracted the attention of scholars due to a series of advantages such as high stability and strong specificity, as well as the significant progress they have made in terms of biomedical applications. However, there are few reports that systematically and comprehensively summarize its working principles, classification and application. In this review, we primarily introduce functional modes of biosensors that combine functional nucleic acids with different signal output modes. In addition, the mechanisms of action of several media of the FNA biosensor are introduced. Finally, the practical application and existing problems of FNA sensors are discussed, and the future development directions and application prospects of functional nucleic acid sensors are prospected.