Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model...

Area of Science:

• Veterinary Pathology
• Artificial Intelligence
• Medical Imaging Analysis

Background:

• Artificial intelligence (AI) presents transformative potential in veterinary pathology, impacting tasks from cell enumeration to personalized treatment.
• Ensuring the safety, efficacy, and dependability of AI systems (AIS) necessitates rigorous preclinical testing and validation.
• The "AI chasm"—the gap between AI model performance in research and real-world applications—poses a significant challenge.

Purpose of the Study:

• To review challenges in AI development and implementation within veterinary pathology.
• To underscore the importance of tailored quality assurance measures for AI in veterinary diagnostics.
• To advocate for a multidisciplinary approach to AI integration, addressing technical, ethical, and regulatory hurdles.

Main Methods:

• Review of current literature on AI applications in veterinary pathology.
• Analysis of challenges including the AI chasm, data privacy, and algorithmic bias.
• Discussion of the necessity for collaboration among veterinarians, computer scientists, and ethicists.

Main Results:

• AI holds promise for diverse veterinary pathology tasks, including cancer detection and prognosis.
• Significant challenges exist in validating AI systems for reliable real-world veterinary applications.
• Ethical considerations such as data privacy and algorithmic bias require careful management.

Conclusions:

• A multidisciplinary approach and tailored quality assurance are crucial for successful AI integration in veterinary pathology.
• Bridging the AI chasm requires addressing technical, ethical, and regulatory issues.
• Balancing AI's potential with risk mitigation is essential for animal welfare and the veterinary profession.