The Adoption Paradox: A Comparative Analysis of Veterinary AI Adoption in China and the North America

• URL: http://arxiv.org/abs/2510.11758v1
• Date: Mon, 13 Oct 2025 01:50:11 GMT
• Title: The Adoption Paradox: A Comparative Analysis of Veterinary AI Adoption in China and the North America
• Authors: Shumin Li, Xiaoyun Lai,
• Abstract summary: This study compares the perception, adoption, and application of artificial intelligence among veterinary professionals in China and North America.<n>Concerns about AI reliability and accuracy were the top barrier in both groups.<n> tailored, region-specific strategies are necessary to responsibly incorporate AI into global veterinary practice.
• Score: 0.017188280334580194
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study compares the perception, adoption, and application of artificial intelligence (AI) among veterinary professionals in China and North America (NA), testing the hypothesis that adoption patterns are shaped by regional market and demographic factors. A descriptive, cross-sectional survey was conducted with 455 veterinary professionals in China between May and July 2025. The results were compared with published data from a 2024 survey of 3,968 veterinary professionals in the United States and Canada. The Chinese cohort, primarily composed of clinicians (81.5%), showed a high AI adoption rate (71.0%) despite low familiarity (55.4%). Their AI use was focused on clinical tasks, such as disease diagnosis (50.1%) and prescription calculation (44.8%). In contrast, the NA cohort reported high familiarity (83.8%) but a lower adoption rate (39.2%). Their priorities were administrative, including imaging analysis (39.0%) and record-keeping (39.0%). Concerns about AI reliability and accuracy were the top barrier in both groups. Our findings reveal an "adoption paradox" where the Chinese market demonstrates a practitioner-driven, bottom-up adoption model focused on augmenting clinical efficacy, while the NA market shows a more cautious, structured, top-down integration aimed at improving administrative efficiency. This suggests that a one-size-fits-all approach to AI development and integration is insufficient, and tailored, region-specific strategies are necessary to responsibly incorporate AI into global veterinary practice.

Related papers

• AI, Metacognition, and the Verification Bottleneck: A Three-Wave Longitudinal Study of Human Problem-Solving [0.0]
  This pilot study tracked how generative AI reshapes problem-solving over six months in an academic setting.<n>Results generalize primarily to early-adopter, academically affiliated populations.
  arXiv  Detail & Related papers  (2026-01-21T15:49:04Z)
• Artificial Intelligence in Spanish Gastroenterology: high expectations, limited integration. A national survey [32.505127447635864]
  Artificial intelligence (AI) has emerged as a disruptive innovation in medicine, yet its adoption within gastroenterology remains limited and poorly characterized.<n>We aimed to examine knowledge, practical applications, perceived barriers, and expectations regarding AI among gastroenterology specialists in Spain.
  arXiv  Detail & Related papers  (2026-01-14T16:58:53Z)
• Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries [65.4286079244589]
  Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events.<n>We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data.
  arXiv  Detail & Related papers  (2026-01-07T23:35:24Z)
• On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making [13.982768346782386]
  We study the effects of selective prediction on human decisions in a clinical context.<n>Our findings indicate that selective prediction mitigates the negative effects of inaccurate AI in terms of decision accuracy.
  arXiv  Detail & Related papers  (2025-08-11T04:53:13Z)
• Toward the Autonomous AI Doctor: Quantitative Benchmarking of an Autonomous Agentic AI Versus Board-Certified Clinicians in a Real World Setting [0.0]
  Globally we face a projected shortage of 11 million healthcare practitioners by 2030.<n>No end-to-end autonomous large language model (LLM)-based AI system has been rigorously evaluated in real-world clinical practice.
  arXiv  Detail & Related papers  (2025-06-27T19:04:44Z)
• An Agentic System for Rare Disease Diagnosis with Traceable Reasoning [69.46279475491164]
  We introduce DeepRare, the first rare disease diagnosis agentic system powered by a large language model (LLM)<n>DeepRare generates ranked diagnostic hypotheses for rare diseases, each accompanied by a transparent chain of reasoning.<n>The system demonstrates exceptional diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013 diseases.
  arXiv  Detail & Related papers  (2025-06-25T13:42:26Z)
• A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers [51.45596445363302]
  GlobeReady is a clinician-friendly AI platform that enables fundus disease diagnosis without retraining, fine-tuning, or the needs for technical expertise.<n>We demonstrate high accuracy across imaging modalities: 93.9-98.5% for 11 fundus diseases using color fundus photographs (CPFs) and 87.2-92.7% for 15 fundus diseases using optic coherence tomography ( OCT) scans.<n>By leveraging training-free local feature augmentation, GlobeReady platform effectively mitigates domain shifts across centers and populations.
  arXiv  Detail & Related papers  (2025-04-22T14:17:22Z)
• Medical Hallucinations in Foundation Models and Their Impact on Healthcare [71.15392179084428]
  Hallucinations in foundation models arise from autoregressive training objectives.<n>Top-performing models exceeded 97% accuracy when augmented with chain-of-thought prompting.
  arXiv  Detail & Related papers  (2025-02-26T02:30:44Z)
• Artificial Intelligence to Assess Dental Findings from Panoramic Radiographs -- A Multinational Study [3.8184255731311287]
  We analyzed 6,669 dental panoramic radiographs (DPRs) from three data sets.<n>Performance metrics included sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC)<n>The AI system demonstrated comparable or superior performance to human readers.
  arXiv  Detail & Related papers  (2025-02-14T16:34:21Z)
• AI Workflow, External Validation, and Development in Eye Disease Diagnosis [5.940140611616894]
  AI shows promise in diagnosis accuracy but faces real-world application issues due to insufficient validation in clinical and diverse populations.<n>This study addresses gaps in medical AI downstream accountability through a case study on age-related macular degeneration (AMD) diagnosis and classification severity.
  arXiv  Detail & Related papers  (2024-09-23T15:01:09Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence [79.038671794961]
  We launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK.
  arXiv  Detail & Related papers  (2021-11-18T00:43:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.