The world of veterinary medicine is experiencing an era of large-scale changes driven by the introduction of new digital technologies: from machine vision to sensor networks and intelligent robots. The growth of data volume, diagnostic complexity, and tightening requirements for animal welfare demand fundamentally new solutions. More and more specialists are asking: are artificial intelligence algorithms able to make animal care more efficient and safer than traditional methods? Right now, the assessment of the possibilities and limitations of these technologies is becoming critically important for the future of the industry.

New horizons: the role of artificial intelligence in modern veterinary medicine

Artificial intelligence (AI) and machine learning (ML) are actively being implemented in animal care practice and scientific research in veterinary medicine. These technologies make it possible to automate tasks that were previously performed by humans, increasing the speed and accuracy of diagnostics, condition monitoring, and disease prevention. Key areas of application include:

·   automatic diagnosis of major diseases and pathologies,

·   precision management of livestock and herds,

·   analysis of animal behavior using cameras and sensors,

·   prevention and monitoring of health at early stages.

The terms “machine learning” and “deep neural networks” refer to methods in which a computer learns to find patterns in large data sets, while “sensor systems” are devices that collect information about the physical parameters of the environment or organism for transmission to analytical modules.

Precision livestock management with on-farm technologies

One of the innovative developments is the three-stage livestock monitoring system described by the team of Zhang, Zhao, and co-authors. This technology includes:

·   automatic inventory using animal detection in video images,

·   individual identification by muzzle using facial recognition algorithms,

·   analysis of muzzle expression to assess health status.

The developers applied advanced deep learning methods such as multi-level convolutional processing and reconfigurable blocks to increase the accuracy and speed of the system. For farmers, this means reduced manual labor costs, the ability for early detection of problems, and improved animal welfare without increasing the workload on staff.

Bio-inspired engineering and robotics

Engineers are increasingly turning to nature for inspiration in creating new technical solutions. In the study by Zhang, Sun, and colleagues, a bionic robotic hand was developed that replicates the mechanisms of goose neck movement. This approach makes it possible to combine flexibility and structural stability, which is extremely important when working in confined spaces, for example, during the examination or treatment of animals on the farm.

Analysis of real animal movements helps optimize robotics parameters, opening new paths for the introduction of such systems in agrotechnology, veterinary procedures, and auxiliary services.

Machine learning helps the doctor

Modern veterinary diagnostics increasingly use big data analysis to identify complex pathologies. Flegel and colleagues showed that models based on Bayesian networks and the random forest algorithm are able to predict the development of structural epilepsy in dogs with high accuracy.

During the study, tens of thousands of models were tested, which made it possible to identify key signs of the disease. Although the use of AI does not replace the doctor’s experience, such systems reduce uncertainty and provide additional grounds for decision-making in the clinic.

The approach to using AI in veterinary medicine is similar to how it is used in human medicine. Artificial intelligence can not only suggest a diagnosis but also, based on patient data, provide forecasts of which diseases may manifest in the future.

When it comes to treating people, AI can also partially take on the functions of a psychologist. The introduction of artificial intelligence facilitates access to psychological services, which is very important in modern conditions. The human factor—empathy, nuances of interaction, and ethical responsibility—are still not fully available to machines. However, AI can act as a supplement, especially in the availability of 24/7 support, personalization, and monitoring.

Artificial intelligence is often used for early diagnosis of addictive behavior, including gambling addiction. Gambling today has become as accessible as possible for the mass player. To play, any device with a stable internet connection is enough. At the same time, developers are offering new, increasingly engaging mechanics.

Crash games, the pioneer among which is Aviator, have become an exciting pastime. This is also confirmed by data on the site about the large number of online casinos that offer crash games. Players who are fond of such games should pay close attention to their psychological state. AI can help in detecting signs of gambling addiction at early stages.

As in conventional medicine, in veterinary medicine AI is actively used to make forecasts and identify animal health problems at early stages.

Automation of behavior studies and new tools for animal science

Behavioral research faces the problem of analyzing large volumes of data and the need for precise reproducibility. Farhat and co-authors considered the possibilities of automation in the study of dog behavior and concluded that such systems can significantly increase the reliability and speed of research but require careful implementation. Among the difficulties are noted:

·   the volume and complexity of processed data,

·   the need for algorithm transparency,

·   moderate trust of researchers in automation.

The authors propose a phased introduction of automated methods with an emphasis on repeatability of results and the quality of source data.

Behavioral features on pasture

A comprehensive study by the team of Yang on the Tibetan Plateau combines field observations and meta-analysis to study the behavioral characteristics of yaks. The work took into account:

·   climatic conditions of the region,

·   quality of available feed,

·   individual characteristics of animals.

The results show that pasture behavior is formed by the interaction of these factors, which makes it possible to develop more sustainable grazing management schemes in the context of climate change and environmental instability.

Voice as an indicator of stress

Monitoring animal vocalizations is becoming part of the precision livestock system. Gavojdian and colleagues developed an approach in which low- and high-frequency signals of cows are analyzed to detect manifestations of stress or distress. The use of interpretable deep learning models allows:

·   identification of individual animals by voice,

·   rapid detection of signs of stress,

·   timely response to deterioration of condition.

Such methods increase the accuracy of early warning and contribute to more humane treatment of animals.

Early disease diagnosis with sensor systems

Machine learning models combined with sensor platforms are successfully used for early disease detection. Magana and co-authors studied the automated detection of digital dermatitis in cows—one of the main causes of lameness. The use of optimization tools such as the Tree-Based Pipeline Optimization Tool (TPOT) allows:

·   analysis of behavioral patterns before the appearance of clinical symptoms,

·   formation of automatic alarm signals,

·   resource savings through early intervention.

Benefits and limitations of generative models in climate recommendations

The emergence of large language models such as ChatGPT has sparked a discussion about the possibility of their use in veterinary diagnostics. Abani, De Decker, and colleagues investigated the question: is AI able to give recommendations on animal diagnosis? Specialists emphasize:

·   generative models are based on pattern search but do not possess clinical logic,

·   advantages—quick access to information and automation of routine tasks,

·   risks—possible errors, lack of responsibility, ethical issues of using unreliable sources.

Experts advise using such tools with caution and only under the supervision of specialists.

Advantages, challenges, and ethical issues of AI implementation in veterinary medicine

Modern developments in AI bring the following benefits to the industry:

·   increased efficiency and productivity of farms,

·   acceleration of diagnostics and decision-making,

·   improvement of quality of life and animal health.

However, there remain challenges that require attention:

·   the need to collect high-quality data,

·   transparency and interpretability of models,

·   issues of responsibility and verification of results,

·   ethical aspects of decision automation.

There are ongoing debates in the professional community about the balance between automation and the need for human control.

The need for cooperation and development of standards in the future

Further progress requires the unification of efforts of veterinarians, engineers, biologists, and policymakers. Key issues for development are:

·   ensuring quality control and safety of AI tools,

·   development of standards of transparency and responsibility,

·   maintaining a balance between technology and professional expertise.

Only through joint efforts can a sustainable and humane future for animal husbandry and veterinary medicine be formed.