ArticleFrontiers in veterinary science2025
Integrating artificial intelligence into veterinary education: student perspectives.
Article in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Artificial Intelligence in Veterinary Education: Preparing the Workforce for Clinical Applications in Diagnostics and Animal Health.Veterinary sciences · 2026Review
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- Artificial intelligence in veterinary education: self-perceived knowledge, use, and attitudes among veterinary students in Spain and Portugal.Frontiers in veterinary science · 2026Article
- The adoption paradox for veterinary professionals in China: high use of artificial intelligence despite low familiarity.Frontiers in veterinary science · 2026Article
- Curriculum framework for artificial intelligence literacy in veterinary education.Frontiers in veterinary science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Advancements in technology have fostered a continuous evolution of higher education, driving the adoption of innovative tools, including artificial intelligence (AI). This study explores veterinary students' interest in AI, their training and experiences, and their perceptions on AI integration in veterinary medicine. Methods: A comprehensive survey was administered to veterinary students at the Faculty of Veterinary Medicine of a single international university in Spain, focusing on their experience with AI, their perception of its integration into veterinary education, and their views on its future role in veterinary medicine. Results: Six hundred and four students of 34 nationalities across all academic years answered the survey. Most students were familiar with AI tools and primarily utilize them in academic settings, recognizing AI as a valuable educational resource. The majority believed universities should encourage and regulate AI use. There was a strong desire to integrate AI-related education into the veterinary curriculum, with students eager to learn more about specific AI applications in various veterinary fields, in particular clinical patient monitoring and veterinary management. The study also highlights the need for training in AI principles and regulation. Likewise, students expressed concerns about ethical and responsible use of AI, as well as the reliability of AI responses. Discussion: This study underscores the importance of integrating AI training in veterinary education to enhance students' competencies. By providing targeted training and support, universities can help students harness the potential of AI while ensuring its ethical and effective use in their careers. This research emphasizes the need for continuous curriculum adaptation to keep pace with technological advancements and meet the evolving demands of veterinary medicine education.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.