TECHNOLOGY AND CODE article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1526820
Automating Updates for Scoping Reviews on the Environmental Drivers of Human and Animal Diseases: A Comparative Analysis of AI Methods
Provisionally accepted
- 1INRAE Occitanie Montpellier, Montpellier, France
- 2UMR9000 Territoires, Environnement, Télédétection et Information Spatiale (TETIS), Montpellier, Languedoc-Roussillon, France
- 3National Institute of Health (ISS), Rome, Lazio, Italy
- 4Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Institut National de la Recherche Agronomique (INRA), Montpellier, Languedoc-Roussillon, France
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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) or 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.
Keywords: Scoping review, nlp, LLM, AI, infectus diseases, covariates analysis
Received: 27 Nov 2024; Accepted: 13 May 2025.
Copyright: © 2025 Decoupes, Cataldo, Busani, Roche and Teisseire. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Rémy Decoupes, INRAE Occitanie Montpellier, Montpellier, France
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.