AUTHOR=Decoupes Rémy , Cataldo Claudia , Busani Luca , Roche Mathieu , Teisseire Maguelonne 

TITLE=Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1526820

DOI=10.3389/frai.2025.1526820

ISSN=2624-8212

ABSTRACT=Understanding the environmental factors that facilitate the occurrence and spread of infectious diseases in animals is crucial for risk prediction. As part of the H2020 Monitoring Outbreaks for Disease Surveillance in a Data Science Context (MOOD) project, scoping literature reviews have been conducted for various diseases. However, pathogens continuously mutate and generate variants with different sensitivities to these factors, necessitating regular updates to these reviews. In this paper, we propose to evaluate the potential benefits of artificial intelligence (AI) for updating such scoping reviews. We thus compare different combinations of AI methods for solving this task. These methods utilize generative large language models (LLMs) and lighter language models to automatically identify risk factors in scientific articles.