The more we share information about what’s happening with artificial intelligence in veterinary medicine, the more that we realize that we need to share more. We’re creating a weekly roundup of Veterinary AI that we’ll be posting here each Friday. We’ll explore perennial issues and emerging trends each week. If you have feedback – things that you would like to hear more about, corrections/redactions, etc. let us know at vic@navc.com.
Cornell University and VetRec: Transforming Veterinary Documentation
Cornell University’s College of Veterinary Medicine has partnered with VetRec, an AI-powered scribe platform, to revolutionize how clinical notes are generated (dvm360). VetRec’s technology creates HIPAA-compliant SOAP notes and discharge summaries, enhancing the efficiency and quality of veterinary care. The platform’s ability to quickly summarize extensive patient histories has been praised by veterinary professionals for improving record-keeping and client engagement. This partnership underscores Cornell’s commitment to integrating cutting-edge technology into veterinary education.
Challenges: Despite its benefits, there are concerns about over-reliance on AI for clinical notes. Veterinarians must ensure that AI-generated notes are accurate and comprehensive, as the final responsibility for medical records remains with them. Moreover, ethical and legal considerations, such as obtaining client consent for recordings, are crucial (Veterinary Practice News).
CloudLIMS and AI in Veterinary Diagnostics
CloudLIMS is set to present at the 2024 ACVP/ASVCP Annual Meeting about the role of AI in enhancing veterinary diagnostics (CloudLIMS). CEO Arun Apte will discuss how AI can revolutionize veterinary diagnostics by automating tasks and accurately analyzing complex data. The integration of AI with Laboratory Information Systems (LIS) is expected to improve data management, leading to faster and more accurate clinical decisions.
Challenges: While AI holds promise, the technology’s integration into existing systems can be challenging due to data fragmentation and complexity. Ensuring the interoperability of AI tools with various diagnostic platforms remains a significant hurdle.
AI in Clinical Decision-Making: Insights from Dr. Adele Williams-Xavier
Dr. Adele Williams-Xavier highlights the transformative potential of AI in clinical decision-making (Dr.
Challenges: Many AI tools are still under development, and their effectiveness in real-world scenarios needs further exploration. Ensuring that veterinary teams are adequately trained to interpret and utilize AI outputs is essential for maximizing benefits.
Enhancing Veterinary Education with AI
The Asian Association of Veterinary Schools (AAVS) is hosting a webinar on leveraging AI for case-based learning in veterinary education (AAVS). This initiative aims to integrate AI into educational platforms to improve feedback and engage students with real-world scenarios.
Challenges: While AI can reduce faculty workload and enhance learning experiences, there is a risk that it may depersonalize education. Balancing AI-driven tools with traditional teaching methods is crucial for maintaining educational quality.
Zoetis Expands Vetscan Imagyst with AI Dermatology
Zoetis has introduced an AI dermatology application to its Vetscan Imagyst platform, aiming to enhance diagnostic capabilities for skin conditions in pets (VetClick). This tool promises rapid and accurate results, supporting veterinarians in providing better care.
Challenges: The success of AI in dermatology depends on the quality of data and the accuracy of algorithms. Continuous updates and validation are necessary to ensure the tool remains reliable and effective.