Open Veterinary Journal, (2025), Vol. 15(5): 1880-1894

Review Article

10.5455/OVJ.2025.v15.i5.4

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

Majid Shafi¹, Shabia Shabir², Sami Jan³, Zahoor Ahmad Wani⁴, Mudasir Ali Rather⁵, Yasir Afzal Beigh⁶, Shayaib Ahmad Kamil¹, Masood Saleem Mir⁷, Andleeb Rafiq⁸ and Showkat Ahmad Shah^1*

¹Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India

²Department of computer Sciences, National Institute of Technology, Srinagar, India

³Department of Social and Preventive Medicine, Government. Medical College, Srinagar, India

⁴Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India

⁵Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India

⁶Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India

⁷Associate Director Research, Directorate of Research, SKUAST-K, Srinagar, India

⁸Veterinary Anatomy, Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India

*Corresponding Author: Showkat Ahmad Shah. Faculty of Veterinary Sciences and Animal Husbandry, SKUAST-K, Srinagar, India. E mail: vetshowkat [at] skuastkashmir.ac.in

Submitted: 05/02/2025 Revised: 04/04/2025 Accepted: 25/04/2025 Published: 31/05/2025

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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.

Keywords: Avian influenza, Artificial intelligence, Zoonotic transmission, Diagnosis, Biosensors, Surveillance, Early detection.

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Introduction

The global population is expected to exceed 9 billion by 2050, leading to an unprecedented rise in food demand, especially for protein sources that are vital for human nutrition (Fraser et al., 2016). The growing Asian wealth is further fueling this demand, with poultry becoming a popular and efficient protein choice. Consequently, poultry production is projected to double from 2005 levels, and global chicken egg consumption is expected to increase by 40% (Smith et al., 2015). Poultry farming will have to expand to meet these demands, leading to larger farms with denser populations of birds. However, this intensification brings its own set of challenges, as research indicates that intensive livestock systems are more vulnerable to disease outbreaks, which can affect both farmed animals and human health (Liverani et al., 2013). Technological developments provide potential approaches to managing vast poultry populations despite these challenges. Many monitoring tools allow farmers to gather and analyze real-time data, enhancing their decision-making regarding bird health and environmental conditions. These technologies enable early detection of infectious diseases, significantly lowering the risk of outbreaks by providing timely insights into the health of poultry.

Infections at poultry farms must be treated promptly to improve production. Different poultry pathogens, like the avian influenza virus (AIV), present significant risks of human infection and potential pandemics. Other bacterial threats, such as Escherichia coli, Salmonella, and Campylobacter jejuni, also endanger human health (Kelland, 2017). The increasing occurrence of emerging diseases in both livestock and humans is associated with increased interactions among wild species, livestock, and humans. These factors increase the probability that human populations will be affected by novel infectious diseases. Increased poultry production and bird density can raise the danger of disease transmission from birds to humans, as demonstrated by the rise of the highly virulent H7N9 AIV in China. Effective methods for quick disease diagnosis and identification are required to reduce these hazards, as are predictive technologies that allow for early therapies. The 2014 outbreak of highly pathogenic avian influenza H5N2 in British Columbia highlights the repercussions of delayed response measures. A 5-day lag between the initial signs of infection and the implementation of quarantine allowed the virus to spread widely, leading to the culling of over 48 million birds in Canada and the USA (Shriner et al., 2016). This situation highlights the critical need for faster detection and response systems.

Avian influenza is a highly contagious viral infection that affects both domestic poultry and wild bird populations. It is caused by influenza A virus from the Orthomyxoviridae family and is divided into two categories: low pathogenic avian influenza (LPAI) and highly pathogenic avian influenza (HPAI). LPAI typically leads to mild respiratory issues and a decrease in egg production, whereas HPAI is much more severe, often resulting in high mortality rates and rapid spread (Erica Kintz et al., 2024). The economic and public health risks of avian influenza are significant, as outbreaks can devastate poultry farms, disrupt food supply chains, and trigger strict trade restrictions (Maciej et al., 2013). Additionally, Avian influenza poses a zoonotic risk, with some strains capable of crossing species barriers and causing serious respiratory illnesses in humans. Avian influenza is mainly transmitted through direct contact with infected birds, their secretions, or contaminated surfaces, including equipment, feed, and water sources. Airborne particles can also contribute to the rapid spread of infectious diseases, especially in densely populated poultry farms (Ferguson et al., 2001). Migratory waterfowl are essential for the global spread of Avian influenza because they can carry and shed the virus over long distances without showing significant symptoms. Once Avian influenza is introduced to a poultry farm, it can spread quickly, often requiring mass culling to control the outbreak. The economic impact of Avian influenza outbreaks includes direct losses from bird deaths, as well as indirect costs related to import or export bans, trade disruptions, and increased biosecurity measures (McLeod, 2010).

Avian influenza has a huge impact on poultry, so it is essential to identify it early in order to stop its spread and minimize mortality. Although traditional diagnostic methods are effective, they often depend on laboratory facilities, specialized staff, and considerable time to confirm infections. Delayed diagnosis can lead to larger outbreaks and higher mortality rates. The recent advancements in artificial intelligence (AI) have transformed disease detection and management (Alowais et al., 2023). AI-driven technologies use machine learning algorithms, big data analysis, and real-time monitoring systems to improve diagnostic speed and accuracy. These tools can sift through extensive datasets, identify early signs of infection, forecast outbreak trends, and automate lab-based diagnostics (Fallahtah and Adekola, 2024). AI-enhanced imaging, biosensors, and cloud-based diagnostic platforms are revolutionizing disease detection and management, providing quick, cost-effective, and highly precise solutions (Wang et al., 2023).

The incorporation of AI into avian influenza surveillance not only boosts detection accuracy but also facilitates rapid responses, reducing economic losses and protecting both poultry health and public safety (Duan et al., 2023). Emerging technologies like biosensors, wearable devices, and noninvasive monitoring tools are transforming how we manage poultry health. These systems allow real-time disease detection, thereby enabling quick on-site diagnoses and facilitating immediate actions, such as quarantining affected farms to prevent further spread (Alexis et al., 2024). Moreover, AI systems can identify sick birds and the pathogens involved, thereby supporting targeted interventions. The ongoing research is focused on improving these devices, each with its own unique strengths and weaknesses. The predictive models that utilize various data sources enhance disease forecasting. The important factors for these models include environmental and geographical elements like climate, livestock density, and the distribution of reservoir host species. Additionally, unconventional data sources, such as web search trends and social media analytics, have shown effectiveness in tracking human influenza and could be adapted for monitoring poultry diseases. By combining rapid detection technologies with big data analytics, decision-support systems can help producers assess and manage disease risks more effectively.

This review explores the scientific progress made by technologies for detecting and predicting diseases in poultry farming, highlighting their potential to reduce the risks of infectious diseases. By utilizing an AI system, the poultry industry can strengthen its capacity to anticipate and tackle new disease challenges, ultimately resulting in better health outcomes for both animals and humans.

Traditional diagnostic methods for avian influenza outbreaks

A combination of clinical observation, molecular diagnostics, serological tests, and virus isolation is typically used to diagnose avian influenza. These traditional methods have been used for many years to identify and confirm the virus’s presence in poultry populations. Although they are effective, they have certain limitations, such as the requirement for specialized equipment, lengthy procedures, and possible delays in implementing disease control measures. Quick and precise detection is vital for preventing avian influenza outbreaks, reducing economic losses, and safeguarding public health (Knobler et al., 2005). The first step in diagnosing avian influenza is clinical observation, in which veterinarians and poultry farmers assess the visible symptoms of infected birds. The common signs of avian influenza include coughing, sneezing, nasal discharge, diarrhea, lethargy, swollen wattles and combs, decreased egg production, and, in severe cases, sudden death (Sendor et al., 2023). Highly pathogenic avian influenza strains can lead to serious systemic infections, resulting in high mortality rates among affected flocks. However, a significant drawback of clinical observation is that the symptoms of avian influenza are nonspecific and can mimic those of other poultry diseases, such as Newcastle disease, infectious bronchitis, and fowl cholera. In some instances, infected birds may not exhibit clear signs of illness, thereby making it challenging to rely solely on clinical symptoms for an accurate diagnosis.

Serological testing is essential for detecting antibodies generated in response to AI infection. The hemagglutination inhibition (HI) test and enzyme-linked immunosorbent assay (ELISA) are the most commonly used serological methods (Comin et al., 2013). The HI test evaluates the ability of AI-specific antibodies to prevent the binding of viral hemagglutinin to red blood cells, offering a quantitative measure of antibody levels. ELISA is a more automated method that detects and quantifies specific antibodies against avian influenza viruses. Serological tests are valuable for surveillance and assessing prior exposure to avian influenza, but they cannot distinguish between active infections and past exposures. Moreover, these tests require a certain period for antibodies to develop, making them less suitable for early detection of outbreaks. The molecular diagnostic techniques, especially reverse transcription-polymerase chain reaction and real-time RT-PCR quantitative reverese transcription polymerase chain reaction (qRT-PCR), have transformed avian influenza detection by offering high sensitivity and specificity. RT-PCR identifies viral RNA by amplifying the virus’s genetic material, enabling quick and accurate identification of avian influenza strains (Spackman, 2014). Real-time RT-PCR enhances this process by quantifying viral RNA as it is being amplified, which shortens processing time and improves diagnostic accuracy. These molecular techniques are essential for differentiating between various avian influenza subtypes and tracking genetic mutations in circulating strains. However, RT-PCR and qRT–PCR require well-equipped laboratories, trained staff, and specialized reagents, which may not be accessible in remote or resource-limited areas. The virus isolation is still considered the gold standard for confirming avian influenza infections because it allows for the direct identification and characterization of the virus. This process involves inoculating embryonated chicken eggs with suspected virus samples and observing viral replication. The isolated virus can then undergo further analysis through molecular and antigenic characterization to assess its pathogenicity (Woolcock, 2008). Although virus isolation offers the most definitive confirmation, it is a time-consuming and labor-intensive procedure that requires biosafety level 3 facilities to prevent accidental exposure and contamination. Virus isolation typically serves for research and epidemiological studies instead of routine diagnosis because of various difficulties, like specialized labs, skilled workers, and time-consuming procedures. These traditional diagnostic methods for Avian influenza are effective, but they also have limitations that should be researched to make them more effective.

Artificial intelligence-Driven diagnostic tools in avian influenza detection

AI has revolutionized the diagnosis and treatment of avian influenza by greatly improving the accessibility, speed, and accuracy of detection methods. In the past, diagnosing avian influenza involved lengthy processes such as clinical observations, laboratory tests, and virus isolation, all of which required specialized facilities, trained personnel, and considerable resources. In contrast, AI-driven diagnostic tools utilize advanced computational techniques, image processing, and deep learning algorithms, providing a much more efficient and precise way to identify avian influenza infections (Alexis et al., 2024). AI models can sift through large amounts of data from various sources, including clinical signs in poultry, lab diagnostic results, environmental factors, such as temperature and humidity, and even historical outbreak patterns. This capability to analyze extensive and complex datasets allows AI to detect subtle patterns or trends that human experts may overlook. Minor changes in symptoms or environmental conditions that may appear trivial can be recognized as early warning signs of a potential outbreak; thus, AI systems can accurately predict these risks before a widespread epidemic occurs. These predictive abilities mark a significant advancement in disease prevention, offering the chance for early interventions and targeted control measures that can help prevent the spread of infection among other poultry populations (Musa et al., 2024).

In addition to its predictive capabilities, AI-driven diagnostics greatly improve the speed of detecting avian influenza compared to traditional methods, especially those relying on virus isolation and serological tests, which can take several days or even weeks to produce results, resulting in significant delays when dealing with rapidly spreading infectious diseases. On the other hand, AI systems can provide analysis almost instantly, reducing the time required to detect affected flocks and enabling prompt action to prevent the spread of the virus (Knobler et al., 2005). This quick response is essential for preventing the spread of the virus across farms and regions, as timely identification facilitates more effective quarantine measures, culling of infected birds, and the implementation of biosecurity protocols. Consequently, AI-driven tools not only help reduce the risk of larger outbreaks and lead to cost savings by minimizing the need for extended laboratory testing and lowering the resources required for field surveillance.

A notable benefit of AI-driven diagnostics is their capacity to minimize human error. Human interpretation of symptoms and laboratory results is often subjective, and misdiagnosis can occur due to similarities between avian influenza and other poultry diseases, such as Newcastle disease and fowl cholera. AI systems are based on data-driven algorithms that produce objective, reliable, and repeatable outcomes, eliminating the chance of misinterpretation. Additionally, AI models can be trained on a wide array of diverse datasets, improving their ability to accurately distinguish between avian influenza and other diseases based on clinical presentation, even when symptoms are subtle or atypical (Walsh et al., 2019).

One major advantage of AI technology is its ability to monitor events in real time. When implemented in a poultry farming operation or surveillance system, AI can continuously analyze data from various sources, such as live sensor readings and health reports. This ongoing flow of information enables AI tools to notice subtle changes in poultry health or environmental conditions that might signal the beginning of an outbreak. AI can identify a sudden drop in egg production, an increase in respiratory issues among birds, or changes in environmental factors that align with the spread of disease (Hassan and Abdul-Careem, 2020). By identifying these early warning signs, AI systems allow for a proactive approach to managing diseases, ensuring that veterinary teams, farmers, and public health officials are notified before the situation worsens. This constant real-time analysis also facilitates quicker decision-making, enabling targeted interventions and a faster response to new outbreaks. The use of AI in detecting and managing avian influenza marks a significant shift in the manner in which poultry health is monitored and controlled. It transitions from traditional reactive methods to a more dynamic, data-driven, and anticipatory strategy. As AI technologies continue to advance, they are set to further transform the industry, enhancing outbreak prevention, optimizing resource allocation, and improving disease control strategies in poultry farming. Thus, AI’s capacity to deliver timely, precise, and actionable insights is reshaping the management of avian influenza, making it more adaptable and resilient to the challenges posed by this complex and evolving viral threat.

Machine learning models for early detection

Artificial intelligence has revolutionized the diagnosis of avian influenza, significantly enhancing the speed, accuracy, and accessibility of detection methods. In the past, diagnosing avian influenza required time-consuming procedures such as laboratory testing, clinical observation, and virus isolation, all of which required specialized infrastructure and significant financial resources (Parvin and Abozar, 2024). On the other hand, AI-driven diagnostic technologies offer a far more effective and accurate means of detecting avian influenza infections by utilizing sophisticated computational methods and deep learning algorithms (Sadegh et al., 2023). By leveraging machine learning, AI models can sift through large amounts of data from various sources, including clinical signs in poultry, laboratory diagnostic results, environmental factors, and historical outbreak patterns. This capability to analyze extensive and complex datasets allows AI to detect intricate patterns or trends that human experts may overlook. The subtle changes in symptoms or environmental conditions that may appear trivial on their own can be recognized as early warning signs of a potential outbreak; thus, AI systems can predict and highlight these risks effectively before a widespread epidemic occurs. These predictive abilities mark a significant advancement in disease prevention, offering the chance for early interventions and targeted control measures to prevent the spread of infection to other poultry populations (Javad et al., 2023). In contrast, AI tools can deliver near-instantaneous analysis, cutting down the time needed to identify infected flocks and allowing immediate action to contain the virus. This quick response is essential for preventing the spread of the virus across farms and regions, as timely identification facilitates more effective quarantine measures, culling of infected birds, and the implementation of biosecurity protocols. Thus, AI-driven tools not only help reduce the risk of larger outbreaks and lead to cost savings by minimizing the need for extended laboratory testing and lowering the resources required for field surveillance.

AI-powered imaging for avian influenza diagnosis

AI-powered imaging techniques have become a groundbreaking and efficient approach for diagnosing avian influenza, providing quick, noninvasive solutions that enhance disease detection and monitoring (Walsh et al., 2019). A prominent technology in this field is thermal imaging, which uses infrared sensors to detect temperature changes in poultry. Infected birds typically exhibit higher body temperatures due to fever, a common reaction to viral infections like avian influenza. Thermal cameras can detect these unusual temperature patterns in real-time, facilitating the early identification of infected birds even before the onset of clinical signs (Zhenjiang et al., 2023). Early detection is vital for curbing the virus’s spread within flocks because it allows for swift isolation and targeted intervention, thus minimizing the risk of widespread outbreaks. Additionally, thermal imaging presents a noninvasive and stress-free way to monitor bird health, as it can be conducted from a distance without the need to physically handle the birds (Jerem et al., 2015).

In addition to thermal imaging, AI-driven computer vision technology is transforming the way in which avian influenza is detected by analyzing changes in poultry behavior. With the help of high-resolution cameras and machine learning algorithms, AI systems can continuously monitor poultry farms and evaluate the movement patterns, postures, and interactions among birds. Healthy flocks typically exhibit consistent movement patterns, foraging behaviors, and social interactions, whereas deviations from these behaviors can indicate illness (Nakrosis et al., 2023). AI can spot reduced mobility, which might indicate respiratory distress or weakness from infection, or it can identify feeding irregularities, such as a sudden drop in food intake. The early indicator of disease is clustering behavior, in which diseased birds gather in a barn corner or avoid interacting with other birds. By observing these behavioral patterns over time, AI systems can offer early alerts for potential outbreaks, thereby enabling quicker interventions and limiting the spread of infection.

A significant advancement in AI-driven diagnostic tools is the development of microfluidic biochips, commonly known as lab-on-a-chip devices, which facilitate real-time analysis of blood and other biological samples. These devices utilize AI algorithms to examine blood samples for viral particles or antibodies linked to avian influenza. Microfluidic biochips operate by manipulating small volumes of fluid on a chip, conducting complex assays like polymerase chain reaction or immunoassays at the microscopic level. With the integration of AI, these biochips can rapidly analyze data and deliver immediate results, minimizing the need for prolonged laboratory procedures (Wang et al., 2022). Their speed and portability make them perfect for on-site testing in poultry farms or field environments, providing results in hours instead of days. This significantly boosts the efficiency of disease surveillance, allowing for the rapid testing of large groups of birds without the necessity of transporting samples to a central laboratory.

In conjunction, these AI-enhanced imaging techniques present considerable benefits in the fight against avian influenza. They facilitate continuous, large-scale monitoring of poultry health, enabling the observation of entire flocks without requiring extensive manual labor or invasive methods (Shaji and George, 2023). The real-time capabilities of these technologies allow for the quick identification and isolation of infected birds, thereby reducing the risk of widespread transmission and supporting faster containment efforts. Additionally, the ability to remotely monitor poultry health through thermal imaging and computer vision alleviates the pressure on resources, as fewer veterinary professionals are needed on-site at all times. The incorporation of AI into these imaging systems also improves the accuracy and reliability of diagnostics, decreasing the likelihood of human error and ensuring that outbreaks are detected and managed promptly and effectively. In the realm of avian influenza, where swift detection and response are vital, AI-powered imaging technologies play an essential role. AI-powered imaging techniques are a transformative tool, streamlining disease management and improving flock health outcomes.

AI-enhanced molecular diagnostic techniques

Avian influenza detection accuracy, efficiency, and reliability have significantly increased due to recent developments in molecular diagnostic approaches augmented by AI. This progress has transformed traditional methods like RT-PCR and genomic sequencing. AI-assisted techniques enhance diagnostic precision by automating complex data analysis, minimizing human error, and providing quicker and more accurate results that are essential for effective outbreak management. A key development is the incorporation of AI into RT-PCR analysis. RT-PCR is a robust molecular diagnostic tool that identifies viral RNA by amplifying genetic material from samples collected from infected birds. Although RT-PCR is favored for its high sensitivity and specificity, interpretation of the results can be labor-intensive and susceptible to human error, particularly when handling large sample volumes. AI software now aids in analyzing RT-PCR outcomes by automatically interpreting raw data, such as pinpointing specific viral sequences and evaluating infection presence (Ditrani et al., 2006). Machine learning algorithms can detect subtle patterns in genetic data that human analysts may overlook, thereby decreasing both false positives (incorrectly identifying an infection) and false negatives (failing to detect the virus). This improved interpretation not only speeds up the results but also enhances their accuracy, facilitating timely and reliable decisions regarding disease control measures. Additionally, the automation of data analysis accelerates the testing process, allowing for the quick identification of infected flocks, which is vital for prompt containment and preventing further spread (Keiran et al., 2024)

In addition to improving RT-PCR, AI is transforming the field of genomic sequencing for avian influenza diagnostics. Genomic sequencing, which decodes the virus’s RNA to reveal its genetic structure, is essential for understanding the virus’s characteristics, mutations, and evolution. However, the analysis of complex genomic data often requires significant manual effort, making it a slow and labor-intensive process. AI-driven machine learning algorithms now streamline the analysis of viral RNA sequences, allowing for the quick identification of mutations, genetic variants, and new strains (Amiroch et al., 2022). These algorithms can detect even the smallest genetic changes with great accuracy, facilitating the detection of novel or highly pathogenic avian influenza strains. By examining extensive genomic sequence datasets, AI can also uncover trends in the virus’s evolution, aiding researchers in tracking changes and forecasting potential future outbreaks. This capability to rapidly analyze and interpret genomic data enhances surveillance and monitoring, ensuring that the virus’s spread is closely observed and enabling early detection of potentially harmful mutations.

Cloud-based AI diagnostic platforms represent a significant advancement in improving molecular diagnostics for avian influenza (Brown, 2006). These platforms facilitate real-time monitoring of diagnostic results, enable data sharing across different locations, and promote collaboration between veterinary professionals and researchers. By storing diagnostic data and AI-enhanced analysis results in the cloud, veterinary teams can access the latest information on avian influenza outbreaks and exchange insights with colleagues worldwide. This collaborative approach fosters better communication among experts, encourages the sharing of best practices, and strengthens decision-making in response to outbreaks. Additionally, cloud-based platforms support continuous surveillance by integrating data from various diagnostic tools, such as RT-PCR tests, genomic sequencing, and other molecular methods, to provide a comprehensive overview of the virus’s spread and genetic traits (Kuchinski et al., 2024). This integration allows for more effective coordination of containment measures, swift responses to emerging threats, and a more unified disease management strategy on a global scale. AI-driven molecular diagnostics bring numerous benefits in terms of speed, accuracy, and scalability. Incorporating AI into traditional diagnostic workflows accelerates the identification of virus subtypes, which is essential for implementing appropriate containment measures. By pinpointing the specific strain of avian influenza involved in an outbreak, AI enhances the capacity to execute targeted control strategies, such as vaccination or culling of affected birds, based on the virus’s pathogenicity and treatment resistance. Moreover, AI’s capability to analyze large volumes of diagnostic data in real time enhances response times, ensuring that interventions are carried out as swiftly as possible to curb the spread of infection (Naguib et al., 2024). Additionally, AI-driven molecular diagnostics are scalable and can be utilized across large poultry populations, facilitating extensive surveillance and early detection initiatives. The capability to automate data analysis and combine various diagnostic methods simplifies the identification of infected flocks, even in remote or resource-limited areas. This accessibility to advanced diagnostic tools ensures that even small-scale poultry farmers or regions with limited resources can take advantage of the rapid and accurate detection capabilities offered by AI, thereby enhancing global disease control efforts.

Thus, the incorporation of AI into molecular diagnostics for avian influenza marks a significant advancement in the speed, accuracy, and accessibility of disease detection and management. By automating data analysis, minimizing diagnostic errors, enabling real-time collaboration, and facilitating the swift identification of emerging strains, AI boosts the ability of veterinary professionals and public health authorities to respond promptly and effectively to avian influenza outbreaks. These improvements not only safeguard poultry populations but also contribute to broader public health objectives by preventing the spread of avian influenza to humans and other animals.

Influenza virus biosensors for poultry disease diagnosis

Many influenza biosensor technologies are primarily intended for human point-of-care diagnostics, but these methods can also be adapted for use in poultry. Because AIVs replicate in the mucosal tissues of birds, biosensors are essential for effectively identifying the virus in these samples. Some highly pathogenic AIV strains can enter the systemic circulation of chickens, making it equally important to detect the virus in serum samples. Various biosensors and rapid assays for influenza detection have been evaluated using biological samples from poultry. For instance, an impedance biosensor featuring an aptamer recognition element successfully detected H5N1 in chicken tracheal swabs, achieving a detection limit of 1 HA unit/50 µl (Karash et al., 2016). Similarly, impedance biosensors utilizing monoclonal antibodies against the H5 protein have shown sensitivity comparable to RT-PCR when analyzing influenza-infected chicken swabs. Additionally, SPR biosensors have been tested using chicken swab material, revealing that aptamers provide greater specificity than monoclonal antibodies for detecting H5N1 in tracheal swabs (Lin et al., 2015). Other biosensors, including FET and quartz crystal microbalance sensors, have been employed to identify different AIV strains in poultry swabs, each with varying detection limits and sensitivity. Although the majority of biosensors for poultry diagnostics have concentrated on influenza viruses, future advancements should also take into account other significant poultry pathogens. Biosensors and rapid detection assays present exciting opportunities for quickly diagnosing poultry diseases. The advantages of these devices lie in their specificity and speed; however, they usually necessitate manual sampling and are employed only after the clinical signs of illness have manifested. Although biosensors can replace traditional laboratory methods, thereby shortening the time required for an official diagnosis, the reliance on manual sampling remains a drawback. The ultimate aim is to facilitate real-time infection detection in poultry, which would allow for earlier identification of the influenza virus, even prior to the appearance of clinical symptoms (Luo et al., 2018). Current research into wearable sensors, noninvasive monitoring techniques, and methods based on vocalization or imaging is progressing toward this objective. These innovations not only deliver real-time insights into poultry health but also lessen the need for regular human oversight, thereby reducing the risk of introducing infectious agents into poultry populations.

Wearable sensors are increasingly being utilized in livestock management across the agricultural sector for various purposes, such as stress detection, behavioral analysis, physiological monitoring, and the identification of health issues. The rise of precision farming has led to its widespread use in industries like dairy farming, where different devices track the health and production metrics of individual animals (Neethirajan et al., 2017). With internet connectivity, these wearable sensors offer producers real-time insights into animal health and productivity. However, in the poultry industry, the implementation of wearable sensors poses challenges due to the sheer number of birds involved in large-scale operations, making it impractical to equip every bird with a sensor. It is feasible to outfit a portion of the flock, and the data gathered from these birds can help evaluate the overall health of the entire flock (Dallimore et al., 2017). Research into wearable sensors for detecting pathogens in poultry is still in its early phases, primarily focusing on the highly pathogenic H5N1 avian influenza virus (HPAIV). Infected poultry display severe symptoms, including lethargy and, in some instances, fever, which wearable sensors aim to identify. A notable advancement in this area was achieved by developing a wearable sensor that integrates an accelerometer with a thermistor probe. Weighing just 5.2 g, this device allows measuring body temperature without needing direct skin contact and monitoring activity levels. It is powered by a battery designed to last for 2 weeks and operates in 20-second intervals to save energy. When tested on 4-week-old chickens infected with three different H5N1 strains, the sensor was able to detect decreased activity levels and, in some cases, increased body temperatures several hours prior to death (Pantin and Swayne, 2009). This data was used to create an algorithm for predicting infections, which included factors such as body temperature thresholds and activity comparisons over 24 hours. This system allowed for the detection of infections 6–36 hours before death, depending on the strain of the virus. Subsequent refinements resulted in a revised version of the wearable sensor that focused exclusively on activity tracking by removing the thermistor probe, since not all H5N1 strains induce fever, and ensuring skin contact was challenging. This design, which relies solely on an accelerometer, showed quicker infection detection compared to the model that used a thermistor. Wearable sensors for detecting influenza in poultry typically track specific physiological changes during infection, providing valuable early warnings for producers (Okada et al., 2009). By operating continuously and sending data remotely, these devices offer near-real-time alerts about health issues, enabling timely interventions. Early detection allows for the implementation of biosecurity measures, culling of infected birds, or administration of treatments, which helps minimize losses and control potential outbreaks.

Wearable sensors have some limitations in terms of specificity, as changes in activity or temperature can be influenced by factors unrelated to disease. One possible solution is to use wearable sensors for initial health assessments, followed by follow-up with point-of-care biosensors to identify specific pathogens. Additionally, detecting low-pathogenic influenza virus infections can be tricky because they often lead to subclinical symptoms, although some birds might show signs like lethargy depending on the strain. Despite these hurdles, wearable sensors hold great potential for real-time monitoring of poultry diseases. When combined with other noninvasive detection methods, they can greatly improve the oversight and management of poultry health.

Big data and AI in avian influenza surveillance

The combination of big data analytics and AI has transformed the surveillance of avian influenza by significantly improving our ability to monitor, predict, and respond to outbreaks with remarkable speed and accuracy. By integrating extensive datasets with sophisticated AI algorithms, we can gain a deeper understanding of the factors that drive disease transmission, enabling the development of targeted and proactive strategies to prevent and manage outbreaks.

A key advantage of AI-driven big data analytics in disease surveillance is its capacity to process and analyze large volumes of environmental data, including temperature, humidity, and migration patterns. These elements are essential for the spread of avian influenza because the virus flourishes under certain environmental conditions and can be spread by migratory birds traveling long distances. AI algorithms can monitor and evaluate these environmental factors in real time, facilitating the early detection of conditions that may promote the virus’s spread. For instance, a sudden change in temperature or humidity, along with the movement of migratory birds in a specific area, could indicate an increased risk of avian influenza transmission. By identifying these trends, AI systems can forecast potential outbreaks, enabling timely interventions and focused surveillance in high-risk areas (Jung et al., 2023).

In addition to environmental factors, AI-driven analytics play a vital role in monitoring and analyzing data at the farm level. Records such as production rates, mortality rates, vaccination effectiveness, and feed consumption provide essential information for spotting anomalies that may indicate avian influenza. AI algorithms can sift through these records to pinpoint deviations from typical patterns, like a sudden increase in mortality or a decline in egg production, both of which could signal a potential disease outbreak (Taleb et al., 2024). By continuously tracking these metrics, AI systems can catch early warning signs of infection before any clinical symptoms appear in the flock. This enables swift actions, such as isolating infected birds, enforcing biosecurity measures, or launching targeted vaccination campaigns, all of which help curb the virus’s spread to other flocks.

AI can also monitor and analyze data from unconventional sources, such as social media and news reports, to identify early signs of disease outbreaks. Social media platforms, online forums, and news outlets frequently provide real-time updates on health-related incidents, and AI algorithms can scan this vast array of unstructured data to detect patterns and anomalies that may indicate the onset of avian influenza (Walsh et al., 2019). For instance, reports of unusual bird deaths, sudden respiratory illness outbreaks at poultry farms, or shifts in local bird migration patterns can all act as early warning signs of potential outbreaks. By analyzing this information in real time, AI systems can track disease spread across regions, allowing public health authorities and veterinarians to respond more quickly and effectively to emerging threats. This proactive surveillance approach, powered by AI and big data, facilitates the implementation of control measures before an outbreak escalates, thereby reducing the risk of widespread infections.

The use of AI-driven big data analytics to monitor and predict disease outbreaks can significantly improve decision-making for policymakers and veterinarians. By offering real-time insights into disease trends, risk factors, and the effectiveness of current control measures, AI systems empower stakeholders to make more informed and timely decisions regarding disease control and prevention strategies. AI models can forecast the geographical spread of avian influenza, enabling the establishment of targeted quarantine zones, the strategic allocation of vaccination resources, and the prioritization of high-risk areas for surveillance. Additionally, AI analytics can assess the success of ongoing interventions, such as vaccination campaigns and culling efforts, and provide valuable feedback on their effectiveness in managing outbreaks (Ayuti et al., 2024). This data-driven approach ensures efficient resource allocation, necessary adjustments to strategies, and continuous optimization of response efforts for maximum impact. The integration of big data and AI into avian influenza surveillance represents a significant shift in the manner in which the disease is monitored, predicted, and controlled. By analyzing extensive and diverse datasets from environmental factors and farm records to social media and news reports, AI systems can offer a comprehensive understanding of the dynamics of outbreaks. This facilitates quicker identification of potential risks, more targeted interventions, and better informed decision-making at all levels, from local farm management to global policy. AI-driven big data analytics not only enhances the immediate response to outbreaks but also strengthens long-term disease prevention strategies, ultimately lowering the risk of widespread infections and supporting the sustainability of the poultry industry.

AI for predicting zoonotic transmission of avian influenza

Avian influenza continues to pose a significant zoonotic threat because certain strains of the virus can infect humans, potentially leading to public health emergencies. Although the transmission of avian influenza from birds to humans is rare, it can result in severe health issues, including the risk of pandemics. AI is essential in predicting and reducing the zoonotic transmission of avian influenza by utilizing its strengths in data analysis, pattern recognition, and predictive modeling (Pillai et al., 2022). AI technologies are improving our capacity to identify high-risk strains, evaluate human exposure to the virus, and create vaccines to prevent infections.

One of the main ways of AI to predict zoonotic transmission is through machine learning algorithms that process large amounts of genomic and epidemiological data. These algorithms can pinpoint specific mutations in the avian influenza virus that could allow it to infect humans. By examining the genetic sequences of AI strains, AI systems can identify minor changes, or mutations, in the virus genome that might enhance its ability to cross species barriers (Knobler et al., 2005). For instance, a mutation in the hemagglutinin protein, which helps the virus bind to host cells, could change its affinity for human receptors, increasing the likelihood of human infection. By forecasting such mutations before they occur in nature, AI enables researchers and public health officials to monitor and prepare for strains with zoonotic potential, facilitating early detection and more effective containment measures.

AI plays a vital role in models that evaluate the likelihood of human infection based on exposure data. These models take into account various factors, such as the geographical locations of outbreaks, human interactions with infected birds, and environmental conditions that could promote viral transmission. By examining patterns of human exposure to infected poultry and the movement of people and animals, AI systems can assess the risk of human infection in specific areas and populations (Agrebi et al., 2020). This predictive ability enables health authorities to allocate resources and interventions effectively in high-risk regions, including targeted quarantine measures and monitoring individuals who have had direct contact with poultry farms. Furthermore, AI models can utilize data from human health systems, like hospital admissions for respiratory illnesses, to identify early signs of human infection, ensuring the timely implementation of necessary public health measures.

Another important function of AI in predicting zoonotic transmission is its capacity to expedite vaccine development. Traditional vaccine development often involves a slow, manual process of identifying the viral antigens that elicit an immune response in humans. In contrast, AI can forecast antigenic drift, the gradual accumulation of mutations in the virus that may change its surface proteins and impact vaccine effectiveness. AI-driven models can analyze the genetic structure of avian influenza strains and predict which viral proteins are likely to change, enabling researchers to choose the best immunogenic proteins for vaccine formulation. This forward-thinking approach accelerates the vaccine development timeline, ensuring that the vaccine remains effective against both current and emerging virus strains (Sharma et al., 2022). Additionally, AI can refine vaccine delivery strategies, such as determining the optimal dosage or identifying populations most at risk of exposure, thereby improving the overall success of vaccination campaigns.

The use of AI to predict zoonotic diseases, especially avian influenza, greatly improves global preparedness and response efforts. AI systems facilitate the quicker identification of high-risk strains, earlier detection of human infections, and more effective vaccine development, all of which are essential for reducing the chances of pandemics (MacIntyre et al., 2022). By offering predictive observations, AI helps public health authorities respond to outbreaks in a timely and focused manner, thereby curbing the virus’s spread and minimizing its effects on both human and animal populations. In this way, AI not only enhances the accuracy and speed of zoonotic disease prediction and bolsters the overall resilience of global health systems against emerging infectious threats. AI technologies advance, they will certainly take on a more pivotal role in preventing zoonotic diseases like avian influenza from escalating into a global pandemic, leading to a more prepared and responsive public health framework.

Predicting poultry disease with multi-source data integration

Internet-based data sources

The vast availability of internet data presents significant opportunities for various industries and businesses, as well as the ability to analyze such data in real time. The data sources, such as search queries from Google and social media interactions from platforms like Twitter, are invaluable. Researchers have successfully tracked the incidence of human diseases by examining the mentionsu-like symptoms on Twitter or the volume of online searches related to influenza symptoms. These methods depend on individuals who are infected and use the internet to seek information about their health, which results in noticeable shifts in the frequency of specific keywords or phrases. For example, Google Trends, a tool that evaluates search data, can identify regional influenza outbreaks even before official monitoring systems, like those from the Centers for Disease Control and Prevention, catch up (Carneiro and Mylonakis, 2009). Similarly, social media platforms such as Twitter have been utilized to track disease mentions, with a model based on Twitter data able to predict the peak of seasonal influenza epidemics up to 6 weeks ahead with impressive accuracy (Singer, 2017). Although web-based data have proven successful in detecting human disease outbreaks, there are still challenges to address, such as limited internet access in certain communities and the risk of biases in searches or posts concerning tracked keywords that may not be directly relevant to surveillance. Although web-based data have been used for syndrome surveillance and early warning systems for outbreaks in humans, applying this approach to poultry production is unlikely to be straightforward. In contrast to systems that monitor human illnesses, which depend on user-generated data, poultry disease information cannot be collected in the same manner. Nevertheless, research has demonstrated that analyzing Twitter posts can effectively summarize online reports related to AIV surveillance. Scientists created an automated system for data extraction and analysis to monitor Twitter posts about AIV by focusing on four specific keywords (Robertson and Yee, 2016). Their model linked AIV-related posts with confirmed cases of avian influenza reported by the World Organization for Animal Health from 2015 to 2016. Therefore, Twitter could serve as a valuable resource for gathering online reports about AIV, and monitoring these posts in real-time could yield important insights for tracking and predicting AIV outbreaks in poultry. It is essential, however, to acknowledge the differences between internet-based data generated by humans during illness and data concerning poultry disease outbreaks because these distinctions impact the effectiveness of predictive models.

Data-driven environmental factors for early disease detection in poultry

The rise of infectious diseases in humans and poultry is often shaped by environmental factors, especially through interactions with wildlife that can carry zoonotic infections. Approximately 70% of zoonotic diseases affecting humans originate from wildlife, with many of these diseases initially infecting livestock before reaching humans (Sleeman et al., 2017). A notable example is the 2014 outbreak of H5 HPAIV in North America, which was linked to migratory birds carrying the virus from Japan and South Korea to northern Russia and ultimately to North America. Phylogenetic studies have indicated that the virus follows specific migratory bird routes, including the Pacific, Central, and Mississippi flyways (Lee et al., 2015). Surveillance efforts in the U.S. prior to the outbreak showed no signs of HPAIV in wild birds from 2006 to 2009, underscoring the essential role that migratory birds had in the 2015 outbreak and the necessity for enhanced surveillance systems (Deliberto et al., 2009). Better-targeted surveillance strategies, such as utilizing network analyses of tagged migratory birds, could enhance the accuracy of wild bird monitoring by identifying key biological flyways and regions in North America (Buhnerkempe et al., 2016). Additionally, wild bird pathogen surveillance often depends on laboratory methods that can take days or even weeks to produce results. The introduction of rapid detection biosensors for monitoring infectious pathogens like the influenza virus could accelerate these surveillance efforts. By merging rapid diagnostic data with insights into migratory patterns, it may be feasible to pinpoint areas where farmed poultry are at greater risk of infection. By combining data on past low-pathogenic avian influenza infections in wild birds, farm density, and closeness to coastlines, a disease distribution map was developed to assess the risk of HPAIV in California. This method was essential for identifying regions at higher risk of HPAIV outbreaks, enabling more targeted and prompt interventions during such events. In addition, these predictive risk maps can utilize real-time data to provide current and precise forecasts that enhance response strategies for managing infectious disease threats.

Data-driven decision support systems

To accurately predict when and where farmed poultry might be at risk of infection, it is essential for predictive models to combine a variety of data sources into decision support systems. These sources encompass web-based information, environmental data like migratory bird tracking, details about poultry farm locations, and, importantly, data from diagnostic devices and sensors on the farms. By integrating data from multiple farms, each equipped with different devices, a significant amount of information is generated, particularly when surveillance includes media such as images or recordings to assess infection status. In addition, the diversity of the data creates a complex pool for the predictive models to navigate. The most effective model for identifying emerging poultry diseases should be able to process this information in real time, thereby facilitating the swift identification of at-risk areas. This task aligns with the capabilities of big data analytics, which involves managing datasets defined by their volume, variety, and velocity (Bansal et al., 2016). For the poultry industry, big data analytics provides a means to incorporate extensive, diverse datasets into real-time decision support systems, allowing for targeted actions to reduce disease risks in specific regions and increase productivity (Manyika et al., 2011).

Barriers to real-time poultry disease prediction

Creating a unified system to predict disease emergence in poultry presents several challenges, ranging from infrastructure limitations to data governance issues. Currently, the sensors and biosensors designed for diagnosing poultry infections are still in development and require further research to achieve accuracy and effectiveness in commercial poultry environments. A major obstacle is inconsistent Internet access on farms, particularly in rural areas. Even in countries like Canada, many farms struggle with reliable broadband connectivity, which hinders the use of technologies such as wearable sensors that depend on real-time data transmission (CFA, 2018). For predictive models to function effectively in real-time, automated and continuous data collection is essential. Although this is achievable for certain devices like wearable sensors and barn imaging systems, it remains unfeasible for specific biosensors or environmental surveillance data. Moreover, farm operators will require training and skills to effectively manage and utilize these new technologies. Another challenge for predictive models is the need to address biases and noise in data, especially from online sources that often produce irrelevant or misleading information. A significant concern moving forward will be data governance, particularly regarding farmers’ apprehensions about data sharing. Although anonymizing data is an option, methods for identifying anonymized data have become increasingly accessible (Wolfert et al., 2017). Despite the potential advantages that predictive models offer to the poultry industry, numerous challenges persist, highlighting the need for continued innovation and development.

Challenges and ethical considerations in AI-based diagnosis

Although AI has great potential to revolutionize the diagnosis and management of avian influenza, numerous challenges and ethical issues need to be addressed to ensure its effective implementation. These challenges, which include concerns about data privacy, algorithm bias, and accessibility, play an essential role in determining how AI-based diagnostic tools can be responsibly integrated into disease surveillance and control efforts.

A major challenge related to AI in avian influenza diagnosis is data privacy (Emmanuel et al., 2024). AI systems depend on extensive datasets, which encompass sensitive poultry health records, farm performance data, and environmental factors. The process of collecting, storing, and analyzing such data can raise significant privacy concerns, particularly when it involves personally identifiable information or proprietary farm data. For AI-driven diagnostic tools, it is vital to ensure that data are securely stored, encrypted, and anonymized to prevent unauthorized access and potential misuse. Additionally, sharing data across various platforms, such as veterinary health systems, research institutions, and international organizations, poses further risks of data breaches and unauthorized use of confidential information. To mitigate these concerns, strong regulatory frameworks and data protection policies must be established to guarantee that AI systems are utilized responsibly, with careful attention to privacy laws and ethical standards. This includes clear guidelines on data collection, access permissions, and retention periods.

A major challenge is algorithm bias, which can occur when AI models are trained on inadequate or skewed datasets. In the case of avian influenza, diagnostic tools that use machine learning algorithms may produce inaccurate predictions or diagnoses if the training data fails to reflect the diversity of poultry populations, farming practices, or environmental conditions. For instance, AI systems that are primarily trained on data from large commercial farms may struggle to perform effectively in smaller rural farms where conditions vary significantly. Likewise, models developed with limited geographic data may not yield accurate results for regions with different environmental factors or poultry breeds (Nakrosis et al., 2023). This algorithmic bias can result in misdiagnoses, such as overlooking outbreaks in small-scale operations or providing incorrect recommendations for disease control. To reduce this risk, it is essential to train AI models on diverse and representative datasets that encompass the variability observed across poultry farms, regions, and environmental conditions. In addition, ensuring transparency in the development and training of AI models, along with conducting regular audits and evaluations, can help identify and rectify any biases that may emerge.

Accessibility poses a significant challenge to the widespread use of AI-based diagnostic tools, especially in developing regions where avian influenza outbreaks can lead to serious economic repercussions. Although AI systems are powerful, they require specialized infrastructure, such as reliable internet access, advanced computing capabilities, and trained personnel who can effectively operate and interpret the diagnostic results. For small-scale poultry farmers, particularly in low-income or rural areas, meeting these requirements may be unrealistic, hindering the effective implementation of AI in disease surveillance. To ensure that AI tools are beneficial for all stakeholders, including farmers in developing countries, it is essential to design AI systems with affordability and user-friendliness in mind. This could involve creating low-cost, mobile-based diagnostic platforms that require minimal hardware or offering training and support to local veterinarians and farmers. By making AI tools more accessible, we can ensure that disease control measures are available not only to wealthier farms or regions, but also to areas that are at a higher risk of avian influenza outbreaks (Dankwa-Mullan, 2024).

Ethical considerations in implementing AI-based diagnostics are essential to ensure that these technologies are used responsibly and effectively in disease management. In addition to data privacy and algorithmic bias, ethical issues include making sure that AI systems do not exploit vulnerable populations, cause harm, or violate farmers’ rights. This requires careful design, deployment, and monitoring of AI systems to ensure they benefit individual farmers and the wider public health system (Bajwa et al., 2021). Accountability issues also arise that who bears responsibility if an AI-powered diagnostic tool misses an outbreak and causes a massive. In order to tackle these concerns, it is vital to create clear regulatory frameworks to oversee the use of AI in disease diagnosis. These regulations should set standards for the accuracy and reliability of AI systems, outline protocols for validating and updating models, and establish mechanisms to ensure transparency and accountability in their application.

In addition to these ethical challenges, the regulatory landscape must adapt to the swift progress of AI technologies. Governments, international health organizations, and regulatory agencies need to work together to formulate policies that tackle the ethical, legal, and social ramifications of AI in disease surveillance. This could include establishing international standards for AI-driven diagnostics and ensuring that AI tools undergo thorough testing, validation monitoring to comply with established safety and efficacy benchmarks. Moreover, stakeholders should participate in continuous discussions to ensure that ethical aspects, such as fairness, transparency, and public trust, are integrated into the design and implementation of AI technologies.

Tackling these challenges and ethical issues is vital for the effective use of AI-based diagnostics for managing avian influenza. By making sure that AI tools are transparent, accessible, and unbiased while also upholding data privacy and ethical standards, the advantages of AI in disease surveillance can be fully realized. Thus, AI has the capacity to transform avian influenza detection and control, but this potential can only be unlocked if these challenges are addressed proactively to promote a responsible, fair, and sustainable approach to disease management.

Future of AI in avian influenza management

The future of AI in the management of avian influenza is incredibly promising, as new technologies continue to change the way in which we detect, prevent, and control diseases. AI is set to take on an even more essential role in fighting avian influenza by offering more efficient, accurate, and accessible solutions. Innovations in edge AI devices, block chain technology, and AI-driven epidemiological modeling are leading this charge. These advancements will revolutionize how we identify and handle outbreaks, fostering a more proactive and responsive approach to disease surveillance. One of the most exciting advancements in AI for avian influenza management is the rise of edge AI devices. These portable diagnostic tools, equipped with AI algorithms, facilitate real-time, on-site analysis even in the most remote and resource-limited locations. Currently, poultry farmers and veterinary teams often depend on centralized laboratories for AI testing, which can delay diagnosis and slow down the response to an outbreak. In contrast, edge AI devices enable immediate diagnosis on the farm or in the field, significantly reducing the time required to confirm an infection and start containment measures. These devices can be compact and portable and can be fitted with sensors that quickly analyze samples such as nasal swabs or blood, providing instant results. With these tools, even small-scale poultry farmers or those in isolated areas will have access to timely diagnostics, enhancing response speed and lowering the risk of widespread infection. In addition, the incorporation of machine learning into these edge devices allows for continuous improvement, thereby making them more accurate and efficient over time.

Alongside edge AI, block chain technology is becoming a significant asset for enhancing the transparency, security, and reliability of poultry health data (Cozzini et al., 2022). In relation to avian influenza, a blockchain can offer a decentralized, ledger for monitoring poultry health records, vaccination details, and outbreak information. This would improve disease monitoring by guaranteeing that the data used for diagnosis and response are accurate, unchangeable, and accessible to all parties involved, including farmers, veterinarians, and public health officials. AI-driven blockchain systems could establish secure channels for sharing data across different regions and institutions, ensuring that health records remain current and safeguarded against tampering (Galvez et al., 2018). This would also facilitate real-time tracking of poultry movements and vaccinations, enhancing traceability and allowing for quicker identification and isolation of infected flocks. The transparent and auditable nature of a blockchain would foster trust among all stakeholders, ensuring that the data will be utilized for AI diagnostics (Shah et al., 2021). The use of AI in epidemiological modeling will significantly enhance the management of avian influenza by analyzing real-time data to predict future outbreaks and improve preparedness strategies. These sophisticated models draw from various data sources, including environmental conditions like temperature and humidity, migratory bird behaviors, and historical outbreak information, to anticipate where and when outbreaks are most likely to occur. This predictive ability will enable veterinary teams and policymakers to allocate resources more efficiently, organize vaccination campaigns, and implement preventive measures in high-risk areas before an outbreak strikes. Additionally, AI epidemiological models can help pinpoint which strains of avian influenza are most likely to spread and become zoonotic, facilitating targeted surveillance and intervention efforts (Peiris et al., 2007). By delivering accurate, data-driven forecasts, these models will bolster our capacity to respond to outbreaks in real time, thereby minimizing both the economic and public health repercussions of avian influenza.

The future of AI in the management of avian influenza is promising, particularly with the development of advanced surveillance systems that can monitor and analyze various factors in real time. These AI-driven systems have the capability to combine data from farms, weather stations, migration patterns, and human activities to create dynamic models that track the spread of avian influenza. Such comprehensive surveillance would enable ongoing monitoring of poultry health, allowing authorities to notice shifts in flock behavior, mortality rates, or production trends that might indicate the early stages of an outbreak. Additionally, AI systems could send real-time alerts, automatically informing farmers and veterinarians when unusual patterns are detected, which would facilitate swift action to curb further transmission (Alvarez et al., 2024). This continuous monitoring, enhanced by AI, can significantly improve the agility and responsiveness of the entire disease management framework.

Regarding disease prevention, AI is set to enhance vaccine development by sifting through extensive genetic and epidemiological data to forecast which avian influenza strains are likely to mutate (Morris et al., 2018). AI algorithms can detect changes in viral genomes and assess their potential impact on human or animal infections, allowing researchers to create vaccines that target emerging strains before they spread widely. In addition, AI can optimize vaccine distribution strategies, ensuring that resources are allocated to the most vulnerable areas and that vaccines reach the appropriate populations at the right time. AI systems can also evaluate the effectiveness of vaccination campaigns by analyzing health data collected from different sources (Capua and Alexander, 2008).

The future of AI in managing avian influenza is expected to involve deeper collaboration between AI technologies and human expertise. As AI tools advance, they will provide veterinarians, farmers, and public health officials with actionable insights and decision-support capabilities. However, human judgment will remain essential when interpreting data, making ethical choices, and implementing control measures. Rather than replacing human expertise, AI systems can act as valuable tools that enhance decision-making processes. This collaboration between AI and human knowledge will lead to more informed, accurate, and timely responses to avian influenza outbreaks, ultimately improving efforts in disease control and prevention. The future use of AI in avian influenza management presents exciting opportunities, offering more efficient and precise diagnostic, predictive, and preventive strategies. With advancements in edge AI devices, block chain technology, epidemiological modeling, and real-time monitoring, AI will greatly improve our capacity to manage avian influenza outbreaks, mitigate their effects, and safeguard both public health and the poultry industry. By continuing to innovate and tackle existing challenges, AI has the potential to transform our approach to avian influenza management and improve disease surveillance and response more dynamic, proactive, and effective.

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Conclusion

The use of AI in diagnosing avian influenza has improved accuracy, speed, and monitoring, playing an essential role in managing poultry health. AI technologies allow for real-time tracking, quick disease identification, and predictive analysis, which helps in developing more effective disease control strategies. With advances in AI technology, it will be vital for early detection, preventing outbreaks, and reducing zoonotic risks in global disease management. The integration of AI into avian influenza management enables a more effective and proactive approach to disease monitoring and safeguarding both poultry and human health.

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Acknowledgment

None.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Authors contribution

MS, SS, SJ, and SAS drafted the manuscript. ZAW, MAR, YAB, SAK, MSM, and AR revised the manuscript. All authors have read and approved the final version of the manuscript.

Data availability

All data are provided in the manuscript.

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