The Impact of Artificial Intelligence on Animal Health Research

Artificial intelligence integrates a domain-specific language and an agent-based software architecture that improves animal health epidemiological model repeatability, transparency, and adaptability.

FREMONT, CA: Leveraging artificial intelligence (AI) methodologies in animal health (AH) helps addressing highly complicated problems, such as those encountered in quantitative and predictive epidemiology, animal or human precision-based medicine, and the investigation of host-pathogen interactions. AI may contribute to diagnosis and disease case detection, to more reliable predictions and reduced errors, to representing more realistically complex biological systems, and to making computing codes more readable for non-computer scientists. It helps accelerate decisions and enhances the accuracy of risk assessments, better-targeted interventions, and anticipated adverse effects.

Due to the peculiarities of AH systems, data, and analytical objectives, AI research may be stimulated by AH difficulties. With the emergence of several current concepts encouraging a global and multispectral viewpoint in the field of health, AI should contribute to the defracturing of the various disciplines in AH in the direction of more transversal and integrative research. The quality and availability of data at the different organizational levels of living systems and diverse geographical and temporal scales remain a focal topic of AH research. Various types of information are of interest.

AI helps us understand animal epidemiological systems

AI-based technologies have made collecting, storing, and distributing large amounts of data more accessible, necessitating better data analysis. Computer scientists used exponential advances in computing power to develop AI approaches to meet these needs. In recent decades, statistical methods have also advanced in dimensionality reduction, variable selection, model comparison, and combination.

AI approaches often detect signals, patterns, or features (density-dependence in vector-borne transmission) that conventional statistical methods cannot. It helps pathogen transmission in complex system networks, typical of emerging illnesses in tropical, developing settings. Developing interfaces and training with under-resourced countries will enable synergistic effects and measures to predict and combat future disease risks.

The Animal Health mechanistic model provides reliability, repeatability, and flexibility

Understanding and predicting disease propagation requires explicit and comprehensive modeling of the mechanisms involved in AH system dynamics, regardless of scale along a primary production chain.

New software engineering-AI methodologies can improve mechanistic modeling transparency and reproducibility, enabling collaboration between software scientists, modelers, and AH researchers throughout the modeling process.

When sufficiently modular to depict contrasting conditions, mechanistic infection dynamics models can predict the impact of conventional and innovative control strategies. Mechanistic epidemiological models need observational data and biology, epidemiology, evolution, ecology, agronomy, sociology, and economics to evaluate realistic control approaches. Their development can quickly address dependability, transparency, repeatability, and usability issues.

Animal health in biology data analysis

Morphological assessments of cell mobility utilizing supervised, unsupervised, and semi-supervised learning methods aid basic biology and biomedicine research. Classification and sophisticated filters sort massive molecular data nowadays. Exploring metabolic, physiological, and immunological signaling networks and identifying and quantifying metabolites in complex biological mixtures was previously difficult. Develop image processing techniques to research host–pathogen interactions in animal pathology and shorten diagnosis time.

Optimization approaches have enhanced the understanding of prion assemblage fragmentation, reducing the time needed to detect neurodegenerative animal disorders and revealing possible treatment targets. Artificial neural networks and deep learning are replacing traditional prediction methodologies in livestock breeding to improve genetic predictions and phenotypic biology. Complex systems biology and AI-based techniques are revolutionizing immunology to understand immune responses.

Using AI, animal health can derive molecular analysis, individual observation data, or production data. They obtain more significant quantities, beyond herds or small animal groups, such as epidemiological data, demographic events, commercial movements, meteorological data, and land-use occupation. It remains challenging to acquire this massive and heterogeneous data, but a large and diverse amount gets collected. Globalization and large-scale animal trade may necessitate the use of global data in AH, particularly for quantitative epidemiology such as the transcontinental spread of infections, animal genetics, and breed management), posing standardization challenges.