NutriSteppe-AI: Development, Architecture, and Explainable Design of a Large Language Model–Driven Chatbot for Personalized Health Menu Generation.
Background/Objectives: Suboptimal dietary patterns are among the leading modifiable contributors to global morbidity and mortality, particularly in cardiovascular disease, type 2 diabetes mellitus (T2DM), obesity, metabolic syndrome, and hypertension. Digital nutrition platforms have emerged to impr...
| Published in: | Nutrients Vol. 18; no. 14; pp. 2228 - 2245 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
MDPI
Jul2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195811147&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195811147 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726643 B0TT jtl: Nutrients issn: 20726643 maglogo: N pubinfo: dt: Jul2026 vid: 18 iid: 14 pid: 97109 pub: MDPI artinfo: ui: 195811147 195811147 195811147 10.3390/nu18142228 195811147 ppf: 2228 ppct: 17 formats: tig: atl: NutriSteppe-AI: Development, Architecture, and Explainable Design of a Large Language Model–Driven Chatbot for Personalized Health Menu Generation. aug: au: Salkhanova, Akkumis Nabigazinova, Elnura Kaldybay, Aliya Omirbekova, Ayaulym Sabit, Madina Baikonsova, Laura Yergeshbayeva, Raushan Knyazbay, Asyl Chuiko, Timur Yermakova, Irina Bekzhanova, Aisulu Tyulebekova, Gulnara Niyetkaliyeva, Danagul Serikova, Nursaya Sharman, Almaz affil: Kazakh Academy of Nutrition, 66 Klochkov Street, Almaty 050008, Kazakhstan sug: subj: Natural Language Processing Chatbot Individualized Medicine Systems Development Algorithms Digital Health Human Descriptive Statistics Decision Support Systems, Clinical Anthropometry Diet Budgets Macronutrients Energy Metabolism Proteins Dietary Fats Dietary Carbohydrates Funding Source ab: Background/Objectives: Suboptimal dietary patterns are among the leading modifiable contributors to global morbidity and mortality, particularly in cardiovascular disease, type 2 diabetes mellitus (T2DM), obesity, metabolic syndrome, and hypertension. Digital nutrition platforms have emerged to improve adherence to evidence-based dietary strategies; however, many systems lack structured optimization, processing-aware nutrient profiling, and explainable artificial intelligence (AI) mechanisms. The integration of large language models (LLMs) into digital health introduces conversational personalization but also risks hallucination and unsafe outputs without constraint enforcement. This study aimed to describe the system development, architecture, database infrastructure, optimization algorithms, explainability enforcement, and digital health implications of NutriSteppe-AI, a chatbot-first LLM-driven system for personalized health menu generation constrained by deterministic nutrient logic and processing-aware scoring. Methods: NutriSteppe-AI integrates: (1) a multi-source structured nutrient database of 20,000 food products with up to 130 tracked nutrients; (2) energy requirement estimation using the revised Harris-Benedict equation; (3) linear programming-based multi-objective optimization; (4) a Healthy Food Index (HFI; 0.5–5.0 scale) incorporating NOVA processing classification penalties; (5) traffic-light nutrient gating; and (6) a constrained LLM orchestration layer governed by structured API contracts. Algorithmic validation was performed using 10,000 simulated user profiles spanning diverse age, anthropometric, activity, dietary exclusion, and budget parameters. Results: The system achieved 96.8% full constraint satisfaction with macronutrient mean absolute errors of 11.60% (energy), 18.86% (protein), 16.26% (fat), and 20.91% (carbohydrates). Incorporating NOVA processing penalties reduced ultra-processed food HFI scores by 0.73 points (p < 0.001). Median optimized menu HFI improved from 3.6 to 4.3. Median system latency was 1.8 s. Explainability validation confirmed 100% deterministic alignment with zero hallucinated numeric claims. Conclusions: NutriSteppe-AI demonstrates that LLM-driven nutrition chatbots can achieve deterministic, explainable, and clinically aligned performance when governed by structured optimization, processing-aware scoring, and explainability enforcement. This architecture provides scalable digital health infrastructure for cardiometabolic disease prevention in diverse populations. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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