Intelligent evidence synthesis for animal health

Overview

This activity represents a strategic collaboration between the SEBI-L programme and the CAMARADES group at the University of Edinburgh's Medical School. Led by Professor Malcolm MacLeod, CAMARADES (The Collaborative Approach to Meta Analysis and Review of Animal Data from Experimental Studies) is a research group bringing decades of expertise in systematic reviews and meta-analysis of animal data. While CAMARADES initially focused on human clinical research, our joint objective is to adapt these rigorous methodologies and advanced digital tools to the livestock sector, fundamentally changing how evidence is gathered, synthesised, and applied for animal health.

The challenge

Traditional evidence synthesis in livestock research is often slow, manual, and limited in scope. Researchers conducting systematic reviews or evidence maps face an overwhelming volume of literature, much of which may be irrelevant. Manual screening and data extraction create bottlenecks, delaying critical insights for policymakers, industry partners, and funders. There is a pressing need to accelerate these processes without compromising the accuracy and reliability required for scientific decision-making.

Our approach: integrating tools and expertise

We are integrating two complementary and innovative technological approaches to streamline the evidence pipeline:

1. Domain-specific search portals: Adapting the SOLES (Systematic Overview of Limited Evidence Synthesis) framework, we are developing dedicated web portals for specific livestock diseases or health topics. Unlike generic search engines, these portals use trained classifiers to aggregate only relevant research papers from global databases. They provide immediate visual statistics on publication trends, open access availability, and data sharing standards, helping researchers quickly assess the landscape of existing evidence before diving into detailed reviews.
2. Advanced screening and extraction: We are leveraging the SyRF (Systematic Review Facility) platform to guide researchers through the systematic review process. By combining AI-driven citation screening—using Large Language Models to evaluate inclusion criteria—with the existing structured data extraction interfaces, our ambition is to significantly reduce the manual burden on researchers. This would allow for the customization of data fields to capture specific metrics, such as disease prevalence or intervention outcomes, with greater speed and precision. SyRF would also enhance our existing LitXpress tool, and our collaboration with CAMARADES will explore additional tools to improve automated research paper classification.

• Learn more about LitXpress 

Strategic value and outcomes

This collaboration is transforming the evidence synthesis landscape by shifting from labour-intensive, narrow-scope reviews to dynamic, automated workflows. By validating and integrating AI-driven tools with established systematic review frameworks, we will enable the processing of vastly larger research corpora with greater efficiency and reproducibility. The vision is a "living" evidence ecosystem that provides real-time insights into animal health research, identifies gaps in current knowledge, and ensures that policy and practice are informed by the most comprehensive and up-to-date data available. This approach not only accelerates the discovery process but also opens up a rich body of evidence that can be used across the livestock sector.

Next Steps

Following initial discussions in early 2026, the SEBI-L and CAMARADES teams are finalising a detailed implementation plan. Key milestones include defining specific disease domains for the first SOLES portal, refining AI prompts for optimal screening performance, and establishing a timeline for integration with the Livestockdata.org platform. This activity exemplifies our commitment to using data innovation to deliver real-world impacts in animal health and welfare.