Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods
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 conduc...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1526820/full |
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| author | Rémy Decoupes Rémy Decoupes Claudia Cataldo Luca Busani Mathieu Roche Mathieu Roche Maguelonne Teisseire Maguelonne Teisseire |
| author_facet | Rémy Decoupes Rémy Decoupes Claudia Cataldo Luca Busani Mathieu Roche Mathieu Roche Maguelonne Teisseire Maguelonne Teisseire |
| author_sort | Rémy Decoupes |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9badd9110fd44ad781425080f9333d4e |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-9badd9110fd44ad781425080f9333d4e2025-08-20T03:10:56ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-06-01810.3389/frai.2025.15268201526820Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methodsRémy Decoupes0Rémy Decoupes1Claudia Cataldo2Luca Busani3Mathieu Roche4Mathieu Roche5Maguelonne Teisseire6Maguelonne Teisseire7Territoires, environnement, télédétection et information spatiale (TETIS), Univ. Montpellier, AgroParisTech, Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), CNRS, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE), Montpellier, FranceINRAE, Montpellier, FranceCenter for Gender-Specific Medicine, Istituto Superiore di Sanitá, Rome, ItalyCenter for Gender-Specific Medicine, Istituto Superiore di Sanitá, Rome, ItalyTerritoires, environnement, télédétection et information spatiale (TETIS), Univ. Montpellier, AgroParisTech, Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), CNRS, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE), Montpellier, FranceCIRAD, Montpellier, FranceTerritoires, environnement, télédétection et information spatiale (TETIS), Univ. Montpellier, AgroParisTech, Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), CNRS, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE), Montpellier, FranceINRAE, Montpellier, FranceUnderstanding 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.https://www.frontiersin.org/articles/10.3389/frai.2025.1526820/fullscoping reviewnatural language processing (NPL)large language models (LLM)artificial intelligence (AI)infectious diseasescovariates analysis |
| spellingShingle | Rémy Decoupes Rémy Decoupes Claudia Cataldo Luca Busani Mathieu Roche Mathieu Roche Maguelonne Teisseire Maguelonne Teisseire Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods Frontiers in Artificial Intelligence scoping review natural language processing (NPL) large language models (LLM) artificial intelligence (AI) infectious diseases covariates analysis |
| title | Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods |
| title_full | Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods |
| title_fullStr | Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods |
| title_full_unstemmed | Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods |
| title_short | Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods |
| title_sort | automating updates for scoping reviews on the environmental drivers of human and animal diseases a comparative analysis of ai methods |
| topic | scoping review natural language processing (NPL) large language models (LLM) artificial intelligence (AI) infectious diseases covariates analysis |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1526820/full |
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