Artificial Intelligence: Transforming Animal Health and Management

Artificial intelligence (AI) has developed as an interdisciplinary science based on computers and is concerned with building machines and equipment which use human intelligence to perform a particular task. Intelligence involves judgment, reasoning, understanding, acumen, insight, comprehension, sharpness, alertness, acuity and also intuition. It is basically a physiological trait present in varying degrees in different animals. The brain carries out cognitive learning and processing by performing combinations of various sort of information processes. Types of information processes are performed by different anatomical structures and instigated in physiology. The intersection of medicine and machine learning has the potential to transform healthcare. Physiology is a foundational discipline of medical training and practice with an upscale of quantitative history, clinical manifestation, diagnostic technics and treatments. The role of Artificial Intelligence is manifold in our day-to-day lives. The history of AI, its applications as software packages, simulation apps, and a list of various equipment used for analytical, clinical, and livestock farm purposes are detailed here. Veterinary practice management software which are commercially available in developed countries, especially for small animal practice, may not be beneficial in the Veterinary Dispensaries and Farms in India due to different conditions, animals and requirements. The AI has immense contribution in Veterinary and allied sciences and has made the diagnosis, treatment, and prognosis quicker, cheaper, and effective. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, the authors have highlighted and discussed the potential impact of various aspects of AI in veterinary health and management, proposing this technology as a key tool for addressing pressing global health challenges across various domains.

Intelligence is defined as the ability to acquire and apply skills and knowledge. It relates to brainpower. How quickly, efficiently and also the manner in which an animal tackles a problem or adverse / favourable situation in its day-to-day life will define its intelligence and mental ability. Intelligence involves judgment, reasoning, understanding, acumen, insight, comprehension, sharpness, alertness, acuity, and also intuition. It is basically a physiological trait present in varying degrees in different animals.

Artificial Intelligence is using the intelligence (brainpower) of human beings to construct and run machines that are smart i.e., make machines do the work that humans need to do, but with greater precision, specificity, efficiency, and quickly. Ever since the time Humans invented the wheel, the wheels of scientific inventions and the development of machines have been turning at a faster rate much ahead of its time. Robotics has made medical science so advanced that the question now arises if robots will replace humans as doctors. The authors attempted to review the literature and probe into Artificial Intelligence in the field of Physiology and Animal Sciences [1]. A major breakthrough in AI came with the development of deep learning, which is a subset of machine-learning that uses artificial neural networks with multiple layers [2]. In other words, Artificial neural networks (ANN) – consisting of interconnected nodes organized in layers are fundamental components of Deep Learning (DL) – which process complex patterns within large data, and which in turn is an advancement of Machine Learning (ML) – which gives sense to machines, and is a subset of Artificial Intelligence (AI) – the replication of human intelligence [3]. Artificial intelligence (AI) is considered as a future disruptive technology that involves the use of computerised algorithms to dissect complicated data [4]. Mahesh et al. [5] opined the foundation of AI models in healthcare have undergone a transformative journey, shedding light on the challenges, ethical considerations, and the vast potential they hold for improving patient outcome and system efficiency. Though initially there was relatively slow adoption of AI within the public sector of healthcare, at present the use of AI in healthcare is unparalleled, especially in the field of diagnosis. The impact of AI vibrates through diagnostic and intervention techniques positioning AI as the cornerstone of precision medicine. AI integrates 5G, IoMT, and blockchain, advancing remote healthcare through connected, data-driven innovations [6].

The brain carries out cognitive learning and processing by performing combinations of various sort of information processes. Types of information processes are performed by different anatomical structures and instigated in physiology. The information processes performed by different major anatomical structures of the brain (cortex, basal ganglia, thalamus, and cerebellum) are important, including their implementations in neuron physiology [7]. Hence, we can conclude that Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence [8]. AI is an umbrella term describing the mimicking of human intelligence by computers [9]. Gardner [10], the American development Psychologist classified intelligence, based on the soft skills possessed by humans, as follows in Table 1.

Table 1: Classification of Intelligence based on soft skills possessed 

Introduction to Artificial Intelligence

Artificial intelligence (AI) has developed as an interdisciplinary science based on computers and is concerned with building machines and equipment which use human intelligence to perform a particular task. It may have multiple approaches to a task involving the cognitive function and performance level of the human brain [11]. The intersection of medicine and machine learning (ML) has the potential to transform healthcare. Physiology, a foundational discipline of medical training and practice with an upscale of quantitative history, might be a start line for the development of a common language between clinicians and ML experts, thereby accelerating real-world impact [12]. AI-based systems have accurately predicted gender from retinal fundus images [13]. Artificial Intelligence has become an integral part of our lives and its involvement has evolved Veterinary Science with respect to quick diagnosis, treatment, and management of animals. AI- based algorithms help in speedier and accurate diagnosis of various conditions. Increased accuracy reduces misdiagnoses and ensures the patient receives correct treatment at the earliest [14]. By maintaining consistent accuracy, AI effectively counters the challenges posed by human fatigue and oversight, ensuring reliable interpretations regardless of external factors [15]. Hence, AI driven systems reduce the time-consuming nature of traditional manual measurements and reduce interobserver variability [16]. Artificial intelligence (AI) serves as the key for transformation of health care, as globally healthcare systems face challenges in the form of escalating costs, limited access, and growing demand for personalized care [17]. The biggest advantage of AI in the Veterinary field lies in its potential to influence health care accessibility by addressing existing resource (material and personnel) constraints especially in the remote villages and providing timely care of the highest quality along with accurate diagnoses. In livestock farming AI has proved to be efficient for predicting production characteristics, individualizing animals, and can be used in breeding programs, especially, those which enhance decision-making in production systems [18].

History of Artificial Intelligence

Various scientists over the years have contributed towards the development of Artificial Intelligence. A chronological list is presented in Table 2 to help the reader understand the progress and development of AI.

Table 2: Chronological history of Artificial Intelligence

The first publication citing the use of computer intelligence in livestock farming was in 1998, and with the advance of automation systems, there has been a steady increase since 2015 in the number of publications. 

Application of Artificial Intelligence in Veterinary Health and Management

The application of Artificial Intelligence in Veterinary Science and health care is manifold. Deep Learning (DL) algorithms help medical imaging technology and support medical practitioners to identify abnormalities and detect diseases with a higher level of precision and speed than ever before, resulting in significant improvements in the accuracy of diagnosis, the efficiency of treatment, and the overall quality of patient care [4]. With AI-powered medical imaging (Table 3), diagnostic changes and applications (Table 4) will result in substantial benefits for the medical and veterinary practitioners and patients (Table 5 & Table 6). 

Table 3: List of First Medical Imaging Applications used (adapted from Dias and Torkamani [8])

Table 4: Areas where AI is utilized in Veterinary Science and allied field

Table 5: Software used in Veterinary medicine and clinical management

AI-driven diagnostic tools (machine learning algorithms, deep learning, and image recognition systems) increase the accuracy and efficiency of disease detection and surveillance. AI also has immense potential to predict disease outbreaks and optimize treatment strategies. AI can also be used to strengthen and speed up the drug discovery and development process. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases [36]. Thus, AI provides a promising solution in analyzing intricate microbiological data quickly and accurately by providing sophisticated computational tools [37].

Table 6: Software used for image analysis of cells, tissues and microbes

Various veterinary practice management software is commercially available in developed countries, especially for small animal practice. However, they may not be useful in the Veterinary Dispensaries in India as the reporting requirements and data obtained are different. Some useful tools for prediction of zoonotic diseases (Table 7).

Table 7: Some examples of useful tools and available webservers dedicated to the prediction of zoonotic/veterinary diseases and monitoring [38] 

AI-based models, in particular deep learning models, could act as effective supports in the evaluation of medical images for both specialized radiologists and general practitioners. Nevertheless, these technologies should not replace veterinary experience and knowledge. On the contrary, AI products have the potential to empower radiologists to deliver increased value in a more efficient way [39]. Some AI applications are also developed for exotic and wildlife animal health monitoring [40] shown in Table -8.

Table 8: AI Applications in Wildlife and Exotic Animal Health Management

Artificial Intelligence in Animal Management

AI-Driven Animal Farming and Livestock Management System represents a pivotal advancement in agricultural technology, promising to revolutionize traditional farming practices by integrating Artificial Intelligence (AI) into livestock management (Table 9). The system's multifaceted capabilities, emphasizing its role in guiding animal farmers towards optimal livestock care, enhancing marketing strategies, and offering a suite of advanced functionalities. At its core, this system employs sophisticated AI algorithms like Natural Language Processing (NLP) and Convolutional Neural Networks (CNNs) to provide personalized guidance to animal farmers, ensuring they adhere to best practices in livestock care. Through real-time monitoring and data analysis, the system offers actionable insights into nutrition, health, and reproduction management, thereby maximizing the well-being and productivity of livestock. Furthermore, the System incorporates innovative features tailored to streamline marketing efforts. By analyzing market trends, consumer preferences, and supply chain dynamics, the system enables farmers to make informed decisions regarding product positioning, pricing strategies, and distribution channels, thereby enhancing market competitiveness and profitability [41]. The future of livestock technology is filled with opportunities to boost productivity, animal welfare, and sustainability in agriculture. Intelligent monitoring, like sensors and IoT devices paired with advanced analytics, provides real-time insights into animal health and environmental conditions. Precision livestock farming, powered by automation and AI, shows potential in optimizing feeding, health monitoring, and breeding [42].

Table 9: Important AI application features in animal management and welfare [43] 

Artificial Intelligence is used in many of the instruments that we use routinely in laboratories, clinics, and farms. Though the list is very exhaustive, the authors have attempted to tabulate some of the commonly used equipment (Table 10). 

Table 10: Some Instruments used in Veterinary Diagnostics

Artificial Intelligence in Simulators

Simulation refers to the artificial representation of the actual process to achieve education through experimental learning. The Society for Simulation in Healthcare, termed simulation training as “the imitation or representation of one act or system by another” which is serves as “a bridge between classroom learning and real-life clinical experience”. A list of some simulators which are used in veterinary health management is tabulated in Table 11.

Table 11: Simulation Apps used in Veterinary Health Management

Role of Ai in Determining Heat Stress in Animals

Livestock encounter various stressors throughout their production cycle, with temperature fluctuations being among the most challenging to manage [74]. Heat stress negatively affects dairy cow performance across all production phases, leading to decreased growth, reproduction, and increased disease susceptibility, ultimately delaying lactation initiation [75]. Instruments used for determine environment stress in animals are listed in Table 12.  Determination of Heat stress in animals plays an important role in animal management and animal welfare.

A. Non-invasive methods

Non-invasive methods for heat stress determination are beneficial as it prevents further stress to the animals. The various methods used to determine heat stress in animals are listed below. 

Infrared Thermography – Evaluation of heat stress responses in Murrah buffaloes, crossbred cattle, and Vechur cattle was done using inner canthus infrared thermography as a non-invasive tool at Silent Valley Farm, Kerala [76]. In Hair sheep the eye temperature was found to have a strong correlation with both vaginal and rectal temperature [77]. However, a variation was found in the degree of correlation between different body regions and ambient temperature when using indirect reflective thermometry (IRT) to measure body temperature accurately [78, 77]. It was observed that in cattle, the forehead IRT showed the strongest correlation with both rectal temperature and THI, which was followed by the flank regions on the right and left [78]. Further, Age, physical condition, lactating stage, and reproductive status of the animals should also be taken into account as a determining factor for IRT readings [79].

Accelerometers – Small, light devices that have minimal impact on animal's normal grazing behavior [80]. Along with the accelerometer Activity collars are fitted on the animal’s neck, which help in detecting the animals breathing pattern. Increased and labored breathing indicates heat stress.

Bioclimatic indices – Bioclimatic indices are the most preferred to measure or predict heat stress in animals. The various indices are listed below

Temperature Humidity Index – Black Globe Temperature Humidity Index [81]

BGHI = BGT + 0.36tdp + 41.5 

Where, BGT: black globe temperature (°C) and tdp: dew point temperature (°C); BGHI below 70 units does not cause discomfort to dairy cow, but decrease in feed intake is observed when BGHI crosses 75 units. Calculated indices include – Heat Load Index [82], Index of Thermal Stress for cows [83], Comprehensive Climate Index [84]. 

Table 12: Equipment used in Environment Physiology to determine Heat Stress

Mobile applications – Nedap Now Herd app can be used to obtain real time thorough summary of the heat stress levels in the sheds. This feature is compatible with IFER(P)4 and IFER(P)9 tags from 2021 and later, requiring the latest Velos version [84]

Feed intake as an indicator of stress. Feed intake can be recorded using automated systems like Insentec feed bins (Roughage Intake Control system, Insentec B.V.) [85].

Milk Yield – In heat stressed dairy cows, variation in milk yield are indicators of herd problems. Milk analyzers like the FT120 (Foss Electric, Hillerod, Denmark) represent a source to automatically highlight variations in milk yield and milk constituents [86].

Behavioral patterns – Shifts in behavioral pattern serve as the first indicator of heat stress and can be detected using mechanized systems which are useful aids to record the behaviour of individual animal [87].

Invasive methods

Invasive methods are accurate and reliable but can be uncomfortable, risky, and are not used regularly or for continuous monitoring.

i) Ear tag sensors – A RFID temperature biosensor (LifeChip Microchip) can be inserted on the rear of the ear base to monitor and record subcutaneous temperature. Subcutaneous temperature sensor is more reliable as it is minimally affected by heat waves and water sprinkling on the body [88].

ii) Rumen Reticulum bolus sensors – They are placed in the reticulum and monitor rumen temperature and pH throughout the day and can wirelessly transfer the data. Names of some Apps that used – SmaXtec classic, Moow Rumen Bolus, LiveCare, Smartstock, Herdstrong.

Advantages of Artificial Intelligence in Veterinary Medicine

Physiological activities like respiratory, perspiratory, cardiovascular responses can be analysed through thermal imaging channels (Thermography) without contact with the individual [89-91].

With the help of simulators help in experimental learning in laboratories and classrooms, of the actual process without the animal [92].

Physiology based medical diagnosis systems are gaining importance in recent years, as they are reliable, accurate, and specific [93].

Artificial Intelligence has made diagnosis cheaper, faster, and easier and helps to recognize the pattern of medical and veterinary complications [94,95]. 

Artificial Intelligence helps to record and store medical information of the individual animal and other animals coming to the clinic.

Diagnostic Apps are developed which use Hybrid Artificial intelligence (Artificial intelligence along with the experience of the Veterinarian) to diagnose ailments through video recordings.

Apps are also developed which read the Veterinarian's notes and suggest the diagnosis and treatment.

Apps are developed to detect Addison’s disease in canines [96].

Artificial intelligence could help a farmer understand if his animal has an emergency and needs treatment. ‘Smart Farms’ can also be developed, which may automatically diagnose sickness and administer remedies/cures to the affected animal (as part of its feed), without any human involvement [97].

Artificial Intelligence removes human bias in diagnosis and treatment, thus improving the skill and efficiency of veterinarians.

Artificial Intelligence will help in automation of tedious tasks on the farm.

The integration of AI into medical education will prepare future health care professionals for the data-driven, technologically advanced health care landscape while fostering a deeper understanding of AI applications and related ethical implications [98].

Artificial Intelligence applications are used in determining the Heat stress that an animal is exposed to.

Artificial Intelligence has made significant progress in Genomics which help in early disease detection. The analysis of vast amounts of genomic data helps to identify genetic mutations that increase the risk of diseases such as cancer, cardiovascular conditions, and hereditary disorders [99].

A wide range of analytical tools (biomarkers) to study biological parameters have been developed to improve animal health and production. It has been established that blood-based biomarker assays aid in the diagnosis of preclinical cardiomyopathy [100].

In the field of companion animal care aspects such as health and behavior monitoring, feed and feeding systems, parasite detection, artificial, virtual, and robotic pets, and veterinary care and support, have been efficiently improved [3].

However, Artificial Intelligence cannot replace a veterinarian and only assists in accurate and speedy teaching, management, diagnosis and treatment.

Challenges Posed by Ai

The use of AI presents various challenges in the veterinary field with regard to disease detection, diagnostics, treatment, production and management (adapted from [3,101,102]). 

The major concern is the quality and representativeness of the data used to train AI models. AI systems that are trained on biased or incomplete datasets, will may produce inaccurate or unreliable results, especially for underrepresented populations. 

Another major concern is regarding data privacy and security, as AI relies on large amounts of sensitive data (In Veterinary Science, data pertaining to animals and owner). 

The third concern is regarding the compliance of ethical standards and regulations, which is crucial for maintaining patient trust and ensuring equitable access to AI-driven diagnostics.

Inherent challenges are posed related to the accessibility of AI-based tools, adaptability, efficiency and flexibility of AI models in veterinary sector under different environment conditions.

Hence, AI should be seen as a tool to assist veterinary professionals rather than replace them. Even as AI gains wider use in Veterinary field, the human elements of empathy, communication, and ethical judgment should remain paramount during treatment and diagnosis. 

Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives [103]. The integration of AI across these diverse healthcare domains reflects its transformative potential in revolutionizing the landscape of medical research and application. There are a number of challenges inherent in veterinary diagnostic imaging data sets. Artificial Intelligence has made deep inroads into Veterinary Medicine. It has become an essential and irreplaceable component and helped evolve Veterinary Science. It is certain that at any point in time, AI may not replace veterinarians, but veterinarians who get trained and wilfully embrace AI will probably surpass those who do not.  It is the need of the hour to provide a common platform for inter-disciplinary research for which AI generative techniques are needed.

The authors declare no conflicts of interest regarding this manuscript.