Large language model-based screening of substances and their composition from safety data sheets for high-resolution chemical exposure assessment.

backgroundSDSs provide information on chemical substances in professional and consumer products. Large language models (LLMs) offer a rapid approach to screening chemical information from SDSs.

objectiveThis study aimed to validate the performance of LLMs in accurately extracting substance information from SDSs of products.

methodsChemical information was extracted from the SDSs of cleaning products using the LLMs ChatGPT-4o and Gemini 2.5 Pro. The performance of the LLMs was evaluated against manually extracted data using precision, sensitivity, and F1 score.

resultsA total of 301 substance-composition combinations across 59 products were included in the validation. The Gemini 2.5 Pro model showed a higher F1 score (1.00) than ChatGPT-4o (0.94). SIGNIFICANCE: LLMs enable high-throughput chemical information extraction from SDSs, reducing the burden of manual screening and supporting large-scale combined-exposure assessments. IMPACT STATEMENT: The accurate identification of chemical substances in professional and consumer products is a major challenge in assessing complex chemical exposures in exposure science and epidemiology. This study validated the use of Large Language Models (LLMs) as a novel, rapid, and highly accurate method of extracting high-throughput chemical data from multilingual Safety Data Sheets. Our findings illustrated that LLMs could be effectively used to overcome the labor-intensive and time-consuming limitations of manual data screening. This approach allows high-throughput analysis, enabling comprehensive combined-exposure assessments in large-scale epidemiological and exposure assessment studies. Additionally, as LLMs can extract data in different languages, they can facilitate international research collaboration.