AI-Driven Data Extraction and AGREE-Based Greenness Evaluation of Analytical Methods for Natural Products: Insights from Selected Studies

Abstract

Large language models (LLMs) have evolved into versatile tools of the 21st century, simplifying repetitive and labor-intensive tasks in everyday life. Here, we aimed to test the feasibility of using different LLMs to assess the greenness of analytical procedures according to the “Analytical GREEnness Metric Approach” (AGREE) by extracting specific data corresponding to the 12 principles of green analytical chemistry from scientific articles. Seven open-access articles on plant natural products using different analytical techniques were evaluated with the five most popular artificial intelligence (AI) tools (ChatGPT, Copilot, Perplexity, Claude, and Gemini), which were tasked to obtain specific data, along with a justification for the selection of this data. Additionally, different versions (basic and advanced) of the same tool (Perplexity and Perplexity Pro), output types (PDF file and link to the online version), and repeatability (three times the same task) were compared. All extracted data were used to calculate AGREE scores, and the final results were compared with those obtained by experts. AI tools were able to assess greenness with a high degree of accuracy, similar to that of trained researchers. Furthermore, a verification/comparison study demonstrated the possibility of critically examining the greenness assessment of developed methods to facilitate standardizing greenness evaluation. The final application of advanced AI tools dedicated to scientific research confirmed the greenness scores, indicating high consistency between all considered evaluation methods.