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

TL;DR

This paper explores how AI can help update literature reviews on environmental factors affecting disease spread in animals.

Contribution

The study evaluates AI methods for automatically updating scoping reviews on environmental disease drivers.

Findings

AI methods were compared for identifying risk factors in scientific articles.

Generative large language models and lighter models were used for this task.

The goal is to automate updates as pathogens evolve and change disease dynamics.

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.

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