20.500.12556/RUL-185271 Large language models in food and nutrition science opportunities, challenges, and the case of FoodyLLM Background Reliable nutrient profiling and semantic interoperability are essential for scalable dietary assessment, food labeling (e.g., traffic-light schemes), and FAIR integration of food composition and consumption data. However, general-purpose large language models (LLMs) are not systematically exposed to structured recipe–nutrition mappings and food ontologies, limiting their accuracy and trustworthiness in food and nutrition tasks. Scope and approach We review recent LLM advances in life sciences and healthcare and analyze the gap in food and nutrition applications. To address this gap, we introduce FoodyLLM, a domain-specialized LLM fine-tuned on 225k task-aligned QA pairs for (i) recipe nutrient estimation, (ii) traffic-light classification, and (iii) ontology-based entity linking to support FAIR food data interoperability. We benchmark FoodyLLM against strong general-purpose baselines (e.g., Llama 3 8B, Gemini 2.0) under zero-/few-shot prompting across five evaluation folds. Key findings Across all tasks, FoodyLLM substantially outperforms general-purpose LLMs for nutrient estimation across all macronutrients (fat, protein, salt, saturates, sugar), accuracy increases from 0.43 to 0.63 to 0.91–0.97; for traffic-light classification across all nutrients and color categories, macro F1 improves from 0.46 to 0.80 to 0.86–0.97; and for ontology-based food entity linking across FoodOn, SNOMED-CT, and Hansard, macro F1 increases from 0.33 to 0.44 (best general-purpose baseline) to 0.93–0.98 on artificial NEL data, and from 0.24 to 0.51 to 0.67–0.84 on real corpora (CafeteriaSA and CafeteriaFCD). Overall, our results demonstrate the practical value of domain-specialized LLMs in food and nutrition research. They enable automated dietary assessment, large-scale nutritional monitoring, and FAIR data integration, while opening new pathways toward sustainable and personalized nutrition. FoodyLLM nutrient estimation data interoperability FoodyLLM interoperabilnost podatkov true false true Angleški jezik Slovenski jezik Članek v reviji 2026-07-30 10:33:47 2026-07-30 10:33:52 2026-08-06 07:41:23 0000-00-00 00:00:00 2026 0 0 26 str. art. 101351 Vol. 12 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8 2665-9271 10.1016/j.crfs.2026.101351 270414595 RAZ_Gjorgjevikj_Ana_2026.pdf RAZ_Gjorgjevikj_Ana_2026.pdf 1 4162ED67E2719E47ECDD80803B24960C 225b476b47401ea3087d341a3a710669ded406aae6dee1bb166aa01b702ab122 7de30937-8bf2-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=243629 https://www.sciencedirect.com/science/article/pii/S2665927126000511 0 1f5f5d76-8bf1-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=243627 Fakulteta za računalništvo in informatiko Medicinska fakulteta 0 0 0