AI showdown: info accuracy on protein quality content in foods from ChatGPT 3.5, ChatGPT 4, bard AI and bing chat

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dc.authoridBAYRAM, HATICE MERVE/0000-0002-7073-2907
dc.contributor.authorBayram, Hatice Merve
dc.contributor.authorOzturkcan, Arda
dc.date.accessioned2024-09-11T19:51:44Z
dc.date.available2024-09-11T19:51:44Z
dc.date.issued2024
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractPurpose - This study aims to assess the effectiveness of different AI models in accurately aggregating information about the protein quality (PQ) content of food items using four artificial intelligence (AI) models - ChatGPT 3.5, ChatGPT 4, Bard AI and Bing Chat. Design/methodology/approach - A total of 22 food items, curated from the Food and Agriculture Organisation (FAO) of the United Nations (UN) report, were input into each model. These items were characterised by their PQ content according to the Digestible Indispensable Amino Acid Score (DIAAS). Findings - Bing Chat was the most accurate AI assistant with a mean accuracy rate of 63.6% for all analyses, followed by ChatGPT 4 with 60.6%. ChatGPT 4 (Cohen's kappa: 0.718, p < 0.001) and ChatGPT 3.5 (Cohen's kappa: 0.636, p: 0.002) showed substantial agreement between baseline and 2nd analysis, whereas they showed a moderate agreement between baseline and 3rd analysis (Cohen's kappa: 0.538, p: 0.011 for ChatGPT 4 and Cohen's kappa: 0.455, p: 0.030 for ChatGPT 3.5). Originality/value - This study provides an initial insight into how emerging AI models assess and classify nutrient content pertinent to nutritional knowledge. Further research into the real-world implementation of AI for nutritional advice is essential as the technology develops.en_US
dc.description.sponsorshipThe manuscript presents a research study that used AI chatbots for its investigations. In particular, it involved the use of ChatGPT versions GPT-3.5 and GPT-4.0, both developed by OpenAI. Additionally, this study used Bing Chat and Bard AI. All authors had access to the data and played a substantial role in writing the manuscript.en_US
dc.identifier.doi10.1108/BFJ-02-2024-0158
dc.identifier.endpage3346en_US
dc.identifier.issn0007-070X
dc.identifier.issn1758-4108
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85197474303en_US
dc.identifier.startpage3335en_US
dc.identifier.urihttps://doi.org/10.1108/BFJ-02-2024-0158
dc.identifier.urihttps://hdl.handle.net/11363/7844
dc.identifier.volume126en_US
dc.identifier.wosWOS:001263254600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Ltden_US
dc.relation.ispartofBritish Food Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectSustainable dieten_US
dc.subjectSustainabilityen_US
dc.subjectArtificial intelligenceen_US
dc.subjectAl modelsen_US
dc.subjectFood assessmenten_US
dc.subjectChatGPTen_US
dc.subjectBard AIen_US
dc.subjectBing chaten_US
dc.titleAI showdown: info accuracy on protein quality content in foods from ChatGPT 3.5, ChatGPT 4, bard AI and bing chaten_US
dc.typeArticleen_US

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