A systematic review on the role of livestock ontologies in animal health management and disease surveillance: A PRISMA 2020 and AI-assisted screening approach

AbstractLivestock farming faces persistent challenges in animal health management, particularly in the surveillance and management of infectious diseases in terrestrial and aquatic species. These diseases affect productivity, economic sustainability, and food security. While smart agriculture and precision livestock farming (PLF) generate large volumes of animal health data, issues such as data fragmentation, poor interoperability, security concerns, and low farmer adoption limit their use. Ontologies as explicit representations of domain knowledge offer a promising way to standardize and integrate heterogeneous data. However, existing literature lacks a comprehensive analysis of their applicability and limitations in livestock disease surveillance. This examines data integration challenges, the role of ontologies, and their limitations in covering livestock diseases. A systematic literature review was conducted following PRISMA 2020 guidelines and supported by a machine learning–based screening tool (ASReview) to ensure transparency, reproducibility, and efficiency in identifying relevant literature. Ontology-based and non-ontology-based approaches were reviewed, with ontologies categorized as active or inactive and assessed for scope, availability, species coverage, and terminological depth. A total of 286 records were screened, of which 100 were included in the final review. Among 32 identified ontologies, 15 remain active while 17 are inactive or no longer publicly accessible, reducing practical use. Active ones often lack full disease coverage across species. Common challenges include system complexity, maintenance, low adoption, and limited domain representation. The review also discusses initiatives such as DECIDE, which illustrate how ontology-driven surveillance can be strengthened through open access, training, and collaborative tools. These findings highlight the urgent need to improve interoperability and develop ontology-driven surveillance systems for livestock.