AI Adoption in Veterinary Medicine: How China and North America Compare
A recent cross-sectional study comparing artificial intelligence adoption between Chinese and North American veterinary professionals reveals striking differences in how AI is being integrated into veterinary practice across the globe. The findings challenge the notion that AI adoption follows a universal pathway and highlight the need for region-specific strategies in technology development and implementation.
Study Overview
Researchers surveyed 455 veterinary professionals in China between May and July 2025 and compared their responses with data from a 2024 survey of 3,968 veterinary professionals in the United States and Canada. The study examined AI familiarity, adoption rates, primary applications, and barriers to implementation.
Key Findings: Two Distinct Adoption Models
The Chinese Model: Practitioner-Driven Clinical Focus
Despite lower self-reported AI familiarity, Chinese veterinary professionals demonstrated remarkably high adoption rates and a clear clinical focus:
Adoption and Usage:
71.0% have used AI tools in their professional work (compared to 39.2% in North America)
Only 55.4% reported familiarity with AI (versus 83.8% in North America)
The cohort consisted primarily of frontline clinicians (81.5% veterinarians)
Primary Applications:
Disease diagnosis and treatment (50.1%)
Prescription calculations (44.8%)
Treatment planning (36.3%)
Administrative tasks ranked lower in priority
Tools of Choice: Chinese veterinarians predominantly used domestic large language models, with Deepseek leading at 67.5% usage, followed by ChatGPT/Gemini and veterinary-specific platforms like MiniVet (both at 20.2%).
The North American Model: Structured Administrative Integration
North American veterinary professionals showed a more measured approach with emphasis on practice management:
Adoption and Usage:
39.2% adoption rate despite 83.8% familiarity
More diverse professional composition (only 24.3% veterinarians, 37.7% technicians/assistants)
Primary Applications:
Imaging and radiology analysis (39.0%)
Record-keeping and administrative tasks (39.0%)
Voice-to-text transcription (36.9%)
Diagnosis and disease detection (34.1%)
Understanding the Disconnect: Familiarity vs. Adoption
One of the study's most intriguing findings is the inverse relationship between familiarity and adoption. Chinese veterinarians are adopting AI at nearly double the rate of their North American counterparts, despite reporting significantly lower familiarity with the technology. This suggests a "learning by doing" approach in China versus a more cautious, knowledge-first approach in North America.
Shared Concerns: The Reliability Question
Despite their different adoption patterns, both groups identified the same top barrier:
Concerns about AI reliability and accuracy:
North America: 70.3%
China: 54.3%
However, North American professionals expressed significantly greater concern about:
Data security and privacy (53.9% vs. 26.6%)
Implementation costs (42.6% vs. 20.4%)
Job displacement fears (36.1% vs. 20.9%)
What Drives Future Adoption?
Chinese respondents overwhelmingly called for more training opportunities (59.1%), reflecting their "adopt first, learn later" approach. Importantly, 93.8% of Chinese veterinarians believe AI should be regulated by veterinary authorities, with nearly equal support for strict regulation (46.6%) and flexible regulation (47.2%).
Clinical Implications for US Veterinarians
This research offers several important insights for American veterinary professionals:
1. Different Markets, Different Needs: The one-size-fits-all approach to AI development is insufficient. Tools designed for one market may not address the priorities of another.
2. The Adoption Gap: North American veterinarians may be overthinking AI adoption. While caution has merit, the Chinese experience suggests that hands-on experimentation can be valuable for understanding AI's practical benefits.
3. Focus Areas: US practices appear to be successfully leveraging AI for workflow efficiency and administrative burden reduction, while Chinese practices are pushing AI directly into clinical decision-making.
4. Training Matters: Regardless of adoption approach, both regions identify education and training as critical factors for successful AI integration.
5. Regulatory Framework: The near-universal call for regulatory oversight in China reflects a global recognition that AI in veterinary medicine requires professional standards and guidelines.
Looking Forward
The study authors emphasize that these findings demonstrate AI adoption in veterinary medicine is not following a single global trajectory. Instead, regional factors including workforce demographics, market maturity, and technological infrastructure are shaping distinct adoption pathways.
For US veterinarians, this research suggests several considerations:
Evaluate current AI usage patterns: Are administrative applications overshadowing potentially valuable clinical decision support tools?
Balance caution with experimentation: While concerns about reliability are valid, they shouldn't prevent thoughtful exploration of AI capabilities.
Advocate for profession-specific AI development: The veterinary field needs AI tools designed specifically for animal healthcare, not just adapted from human medicine.
Support regulatory development: Professional organizations should lead in establishing standards for AI use in veterinary practice.
Bottom Line
AI integration in veterinary medicine is occurring through two distinct pathways: China's practitioner-driven, clinically focused "bottom-up" adoption and North America's structured, administratively oriented "top-down" integration. Neither approach is inherently superior, but understanding these differences is essential for developing effective, region-appropriate AI strategies that genuinely enhance animal healthcare.
As the technology continues to evolve, veterinary professionals worldwide will benefit from cross-regional dialogue about best practices, shared challenges, and responsible implementation strategies. The goal isn't uniformity—it's optimizing AI adoption to meet the specific needs of each veterinary healthcare system while maintaining the highest standards of patient care.
Read full article here: https://www.researchgate.net/publication/396498912_The_Adoption_Paradox_A_Comparative_Analysis_of_Veterinary_AI_Adoption_in_China_and_the_North_America
