# The adoption paradox for veterinary professionals in China: high use of artificial intelligence despite low familiarity

## TL;DR

Chinese veterinary professionals widely use AI despite limited familiarity, highlighting a need for better training and regulation.

## Contribution

First exploratory analysis of AI adoption and perception among Chinese veterinary professionals, revealing an adoption paradox.

## Key findings

- 71% of Chinese veterinary professionals use AI despite 44.6% reporting low familiarity.
- AI is primarily used for clinical tasks like diagnosis and prescription calculation.
- Most users support regulatory oversight due to concerns about AI reliability and accuracy.

## Abstract

The global integration of artificial intelligence (AI) into veterinary medicine is advancing, yet its adoption in major markets like China remains uncharacterized. This study aimed to provide the first exploratory analysis of AI perception and adoption among veterinary professionals in China.

A cross-sectional survey was administered to 455 veterinary professionals in China from May to July 2025. Data on AI familiarity, adoption rates, application priorities, and perceived drivers and barriers were analyzed using descriptive statistics and thematic analysis.

We identified a distinct adoption paradox: 71.0% of respondents incorporated AI into their workflow, yet 44.6% of these active users reported low familiarity with the technology. Adoption was primarily practitioner-driven and focused on core clinical tasks, including AI-assisted disease diagnosis (50.1%) and prescription calculation (44.8%). The primary barrier to use was concern about AI reliability and accuracy (54.3%). A strong consensus (93.8%) emerged supporting regulatory oversight of AI by veterinary authorities.

The adoption paradox is driven by a practitioner-led, “inside-out” integration model where AI is used to augment clinical capabilities, countered by an “interpretability gap” that limits trust and familiarity. This contrasts with the more administrative, “outside-in” pattern seen in North America. The findings underscore a need for specialized veterinary AI tools, enhanced training focused on critical appraisal, and robust regulatory frameworks to safely harness AI’s potential in one of the world’s largest veterinary markets.