Veterinary students exhibit low artificial intelligence literacy but agree it will be deployed to improve veterinary medicine

Abstract

Objective: To determine the perceptions and self-reported knowledge base of AI and machine learning (AI/ML) among professional veterinary students. Methods: First-, second-, third-, and fourth-year professional veterinary students from the School of Veterinary Medicine at the University of California-Davis were surveyed in a cross-sectional study regarding their knowledge level, attitudes, and feelings regarding AI/ML in veterinary medicine. Responses were summarized, and descriptive statistics were performed. Results: One hundred seventy-six of 594 (29.6%) veterinary students responded to the survey. One hundred forty-one out of 176 (80%) students reported slight or no knowledge surrounding AI/ML, and 139/176 (79%) of students were moderately to extremely interested in learning about AI/ML applications in veterinary medicine. Sixty-five out of 176 (37%) students reported learning about AI/ML concepts in their veterinary curriculum. Most students expect to use these tools in their practice (104/176 [59%]) and suspect that AI/ML will improve veterinary medicine (135/176 [77%]). Conclusions: Artificial intelligence and machine learning applications in veterinary medicine are increasingly available. Professional veterinary students are eager to learn about these technologies and recognize their relevance to their future careers. Clinical Relevance: Many professional veterinary programs do not provide structured AI/ML literacy training. Artificial intelligence education should be incorporated into the curriculum to ensure that future veterinarians can critically evaluate and effectively integrate AI/ML tools into clinical practice.

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