AI Transforming Veterinary Medicine

Published Date : 07/06/2025 

BMC Veterinary Research is inviting submissions for a special collection on AI applications in veterinary medicine, highlighting advancements in disease detection, treatment, and prevention. 

BMC Veterinary Research is calling for submissions to our Collection on Artificial Intelligence: Applications in Veterinary Medicine. This initiative aims to explore how advancements in artificial intelligence (AI) are transforming veterinary medicine by enabling novel approaches to disease detection, treatment, and prevention. AI can assist in early diagnosis, risk assessment, and precision medicine by analyzing complex datasets, ultimately improving animal health and welfare.

AI encompasses a range of technologies, including machine learning, deep learning, and natural language processing, which enable computers to analyze vast amounts of data, recognize patterns, and make informed decisions. In veterinary medicine, these technologies are being harnessed to improve diagnostic accuracy, enhance treatment protocols, and streamline practice management. By leveraging AI, veterinary professionals can provide more precise and efficient care, leading to better health outcomes for animals.

The significance of AI in veterinary medicine is underscored by its potential to improve patient outcomes and operational efficiencies. Recent innovations, such as AI-powered animal health monitors and advanced veterinary imaging techniques, have revolutionized diagnostics and treatment protocols. Furthermore, predictive analytics and natural language processing are streamlining communication and decision-making processes within veterinary practices. As we continue to explore these advancements, it becomes increasingly clear that AI has the power to redefine veterinary care, making it more precise and accessible.

We invite submissions of original research articles on the design, implementation, optimization, and clinical applications of AI in veterinary medicine. Topics of interest include, but are not limited to:

- AI-driven diagnostic tools for infectious and non-infectious diseases in animals

- Machine learning (ML) applications for early detection of veterinary diseases

- AI in veterinary imaging and radiology analysis

- Predictive modeling for disease outbreaks and epidemiological surveillance

- AI-powered wearable devices for real-time health monitoring in companion and farm animals

- Natural language processing (NLP) for analyzing electronic veterinary health records

- AI applications in veterinary surgery and anesthesia

- Precision livestock farming: AI for monitoring animal welfare, behavior, and productivity

- AI in veterinary pharmacology and drug discovery

- Automated AI-based risk assessment for zoonotic disease transmission

- Ethical considerations and challenges in the use of AI in veterinary practice

- AI-assisted decision-support tools in veterinary clinical settings

- AI in veterinary pathology

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Image credit: © Don Wu / Getty Images 

Frequently Asked Questions (FAQS): 

Q: What is the main focus of the call for papers?

A: The main focus is on the design, implementation, optimization, and clinical applications of artificial intelligence in veterinary medicine.

Q: What are some key technologies included in AI for veterinary medicine?

A: Key technologies include machine learning, deep learning, and natural language processing, which are used to analyze data, recognize patterns, and make informed decisions.

Q: How does AI improve diagnostic accuracy in veterinary medicine?

A: AI improves diagnostic accuracy by analyzing complex datasets, recognizing patterns, and providing more precise and efficient care, leading to better health outcomes for animals.

Q: What are some examples of AI applications in veterinary medicine?

A: Examples include AI-driven diagnostic tools, machine learning applications for early disease detection, AI in veterinary imaging, and predictive modeling for disease outbreaks.

Q: What ethical considerations are involved in using AI in veterinary practice?

A: Ethical considerations include ensuring data privacy, avoiding bias in algorithms, and addressing the potential impact on veterinary professionals and animal welfare. 

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