Year: 2025
Type: preprint
Abstract: Although artificial intelligence (AI) is increasingly applied to veterinary diagnostics, its real-world implementation faces a unique set of challenges.Unlike in human healthcare where AI systems are developed under structured regulations and validated using large, standardized datasets, veterinary medicine operates with less regulatory oversight, more heterogeneous data, and fewer resources.These conditions hinder the development of trustworthy, generalizable, and ethically sound AI tools.This scoping review critically examines the current landscape of AI in veterinary diagnostics, with a focus on the conceptual and technical challenges that impact its clinical adoption.To map the landscape and refine our search strategy, we initially conducted a manual exploratory search, followed by a systematic search in PubMed Central, which identified studies published as early as 2013 through June 2025.After screening using AI-assisted methods followed by manual review, sixty-three articles met the inclusion criteria and were used for this review.Our analysis categorized challenges in adopting AI in veterinary diagnostics into three groups: data acquisition, model development, and implementation.Issues such as limited data were explicitly acknowledged by most articles, while lack of transparency and external validation were identified as the most common potential limitations.We also highlight possible solutions to support more responsible and effective clinical adoption.
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4413345415