Veterinary medicine has become one of the healthcare fields experiencing the fastest digital transformation in recent years. Artificial intelligence (AI) and software technologies are reshaping veterinarians' workflows in every area from clinical applications to education, from diagnosis to treatment. So what are the scientific foundations of this transformation? In this article, we examine 5 important academic studies published on AI and software use in veterinary medicine, offering clinical applications, education, ethics, and future implications.
The Rise of Software and AI in Veterinary Medicine
The comprehensive study by Elasan et al. published in 2025 analyzes global trends in artificial intelligence use in veterinary medicine. The research examined 1,400 articles published between 1990-2024 and reached striking findings: with 44,700 total citations, each article was cited an average of 32 times and the H-index reached 74. The most important finding is a rapid increase in both article count and citations after 2019.
The COVID-19 pandemic was a turning point that accelerated digitalization in the veterinary sector as well. The study highlights that the USA, Taiwan, and the UK produce 84% of publications, but academic publication counts are very low in some countries. While there is great potential in the veterinary AI field globally, academic publication counts need to be increased and integration into clinical applications accelerated. AI platforms offering multi-language support like Vetigen play an important role in filling this gap.
The Role of Software in Clinical Applications
The most common use of AI software in veterinary clinics is in imaging and diagnostic systems. The 2023 study by Pomerantz et al. evaluated the performance of a commercial AI software, Vetology AI®, in detecting pulmonary nodules and masses in dogs. The study was conducted on 56 confirmed cases and 32 control cases and revealed important findings:
These results show that AI software has high specificity but moderate sensitivity. Therefore, such software should be offered to veterinarians as an assistive tool and the final diagnosis should always be made by the physician. Vetigen's AI-powered radiology analysis module similarly provides decision support to veterinarians while always leaving the final decision to the physician.
Clinical Application Tip
When using AI software, high specificity reduces false positives but moderate sensitivity means some cases may be missed. Always combine with your own clinical assessment.
AI Software Use in Education and Research#
Generative AI tools have started a new era in veterinary education and research. The 2024 study by Chu et al. provides a practical guide on how ChatGPT can be used in clinical, educational, and research areas in veterinary medicine. The study shows how veterinary professionals without programming knowledge can benefit from these technologies.
In clinical practice: ChatGPT can extract patient data, create progress notes, and provide diagnostic support in complex cases. In education: Veterinary educators can create custom GPTs for student support. For example, a tool called VetClinPathGPT helps veterinary students learn disease diagnosis, treatment, and prevention in clinical pathology. In research: ChatGPT can provide support in academic writing processes, but rules set by publishers must be followed.
Important Warning
Chu et al. emphasize that continuous dialogue, awareness of limitations, and regulatory oversight are critical. Generative AI should support, not jeopardize clinical care, educational standards, and academic ethics.
Ethical and Legal Considerations in Software Development#
The rapid increase in AI use in veterinary medicine also brings ethical and legal issues. The 2022 study by Cohen et al. examines the ethical and legal aspects of AI use in veterinary radiology and radiation oncology, starting from the "First, do no harm" principle. The study's most important finding is the lack of regulatory oversight for veterinary AI products. While AI products in human health require validation before going to market, there is no such requirement in the veterinary sector.
The 2022 study by Appleby and Basran raises similar concerns: the opacity (inexplicability) of AI systems may cause veterinarians to be reluctant to trust predictions they don't understand. Therefore, transparency and explainability are essential for veterinary AI software.
In this context, GDPR (Europe) and HIPAA (USA) compliance are also critically important. Patient data, pet owners' personal information, and clinical records must be protected with the highest security standards. Vetigen meets these requirements with ISO 27001 certification and compliant data storage processes.
- AI products should undergo clinical validation before going to market
- Software should be transparent and explainable
- Data protection and security standards must be met
- Veterinarians should take an active role in questioning AI solutions
- Profit motive should not override clinical outcomes and animal welfare
Implications and Future Vision for Veterinary Software Projects
The key lessons from these 5 academic studies shed light on how veterinary software projects should be designed:
Clinical validation: AI models should be tested on real clinical data and performance metrics should be shared transparently.
Ethical standards: The "First, do no harm" principle should always be at the forefront.
Transparency and explainability: AI decisions should be understandable and questionable by veterinarians.
Multi-language support: Native language terminology support is critically important for veterinarians worldwide.
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