A large language model-assisted workflow for generating a living evidence base for climate-sensitive foodborne disease

Abstract Climate change is altering environmental conditions that influence foodborne disease transmission, yet traditional systematic reviews cannot keep pace with expanding evidence. We assessed whether an LLM-assisted workflow could generate a rapid, repeatable, and policy-relevant living evidence base for climate-sensitive foodborne disease. We combined structured PubMed searches (2010-2023), gold-standard human labelling, and iterative refinement of a GPT?4?Turbo?based auto-labeller within the SysRev platform. Pathogens of public-health importance in England were selected a priori. Model performance was evaluated against human reviewers using recall, precision, specificity, accuracy, and balanced accuracy. The refined inclusion model achieved 89{middle dot}2% recall, 59{middle dot}2% precision, 84{middle dot}5% specificity, and 85{middle dot}4% accuracy across 1,044 screened abstracts, identifying 436 studies for inclusion. Post-hoc re-evaluation of discordant abstracts showed that records excluded by the model but included during initial human screening did not meet the refined inclusion criteria. Frequently identified climate exposures included rainfall, temperature, seasonality, and humidity; norovirus, Salmonella, Campylobacter, and Cryptosporidium were the most common pathogens. An LLM-assisted workflow can generate living evidence for climate-sensitive foodborne disease with high recall and improved screening consistency. The approach is scalable, auditable, and suitable for secure institutional environments, supporting horizon scanning and climate-health risk assessment.