Artificial Intelligence in Veterinary Science: Changing the Scope of Animal Healthcare, Veterinary Education, and Livestock Farming Systems

Dr. Jayanta Kumbhakkar

Abstract: Artificial Intelligence (AI) is increasingly transforming veterinary science by integrating computational intelligence, machine learning, deep learning, computer vision, predictive analytics, and intelligent decision-support systems into animal healthcare and veterinary education. This review critically examines AI's expanding role in veterinary diagnostics, epidemiology, precision livestock farming, veterinary education, clinical decision-support systems, and One Health frameworks. Recent advances in machine learning (ML), deep learning (DL), convolutional neural networks (CNNs), natural language processing (NLP), and sensor-based monitoring technologies have significantly enhanced the analysis of complex biological, clinical, imaging, and environmental datasets. AI-assisted systems are now widely used for radiographic interpretation, histopathology, disease forecasting, livestock monitoring, behavioural analysis, and predictive health management. The review also evaluates AI's role in competency-based veterinary education through adaptive learning systems, simulation-based training, intelligent tutoring platforms, and AI-assisted assessment methodologies. India-specific developments, including NADRES disease forecasting systems, smart dairy monitoring technologies, AI-assisted telemedicine, and digital livestock advisory platforms, are critically examined to highlight AI's emerging role in strengthening veterinary services in resource-diverse settings. Despite substantial opportunities, implementation challenges remain significant, including limited veterinary datasets, algorithmic bias, digital inequality, infrastructural limitations, ethical concerns, transparency issues, and regulatory uncertainty. The review further examines the policy implications, data governance requirements, and ethical considerations associated with the deployment of AI in veterinary systems. It concludes that AI should serve as an augmentative and supportive technology rather than a replacement for veterinary expertise and professional judgment. Responsible integration of AI into veterinary science requires interdisciplinary collaboration, institutional reforms, standardised datasets, ethical governance, scientific validation, and continuous capacity building. With appropriate oversight and evidence-based implementation, AI has the potential to substantially improve animal healthcare, veterinary education, livestock sustainability, and global One Health preparedness.

Keywords: Artificial Intelligence, Veterinary Science, Machine Learning, Deep Learning, Precision Livestock Farming, Veterinary Education, One Health, Veterinary Informatics

How to Cite?: Dr. Jayanta Kumbhakkar, "Artificial Intelligence in Veterinary Science: Changing the Scope of Animal Healthcare, Veterinary Education, and Livestock Farming Systems", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 1110-1117, https://www.ijsr.net/getabstract.php?paperid=SR26517192814, DOI: https://dx.dx.doi.org/10.21275/SR26517192814

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