20.500.12556/DiRROS-27977 Large language models in food and nutrition science opportunities, challenges, and the case of FoodyLLM 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 Elsevier Nizozemska Angleški jezik Slovenski jezik © 2026 The Authors. Neznano 2026-03-04 12:39:51 2026-03-04 12:39:51 2026-03-05 03:54:08 0000-00-00 00:00:00 2026 0 0 Nasl. z nasl. zaslona; Soavtorji: Matej Martinc, Gjorgjina Venikj, Jan Drole, Nives Ogrinc, Sašo Džeroski, Barbara Koroušić Seljak, Tome Eftimov; Opis vira z dne 4. 3. 2026; str. 1-26 Vol. 12, [article no.] 101351 2026 0000-00-00 Zaloznikova Objavljeno apc 2025-11-13 2026-02-13 2026-02-16 004.8 2665-9271 10.1016/j.crfs.2026.101351 270414595 https://www.sciencedirect.com/science/article/pii/S2665927126000511?via%3Dihub 1 df801342-17be-11f1-8c73-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=41248 RAZ_Gjorgjevikj_Ana_2026.pdf RAZ_Gjorgjevikj_Ana_2026.pdf Raziskovalni podatki so na voljo na straneh https://github.com/matejMartinc/FoodyLLM, https://huggingface.co/Matej/FoodyLLM in https://zenodo.org/records/17798877. 1 B15A5C3391C3B2DF6F3C98B691CC9D4E 2b68834eccf2847fcc2ac03064a087cd3db1bd07b801970861c14c0880177b63 3e4939ef-17bf-11f1-8c73-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=41249 Institut Jožef Stefan 0 0 0