ArticleMethodsX2025
ChatGPT based method for obtaining repeatable and quantitative colorimetric measurements.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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Abstract
The rise of artificial intelligence (AI) applications in the modern industry has been shown to boost productivity in many sectors such as manufacturing, mining, finance, marketing, pharmacy, textiles, and a few more industries. AI is a neural network system that is wired to learn by interacting with humans and by repetition of tasks. A sub-category of AI common to many ordinary citizens is the Chat Generative Pre-training Transformer (ChatGPT) a program of OpenAI Global, LLC., that generates human-like dialogue and responses from the user input. • ChatGPT can be used to determine colorimetric values for the assessment of environmental pollutants such as congo red (CR) and coomassie brilliant blue (CBB) and prove its ability for consistent reproducibility. • Several color models have been developed such as CIE Lab, RGB, and Xyz to quantify colors. • We used ChatGPT, in conjunction with Color Grab which is an Android app, to quantitatively determine the RGB values of the pollutant solutions, and investigated the color difference ΔE, as well as the hue and chroma that facilitates the color absorption of the pollutants. • The results from ChatGPT and Color Grab were confirmed with the well-established ultra-violet visible (UV-Vis) spectrophotometer.
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