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1718340 Vol 9 · Issue 11 Download Paper

NutriAI Pro – An AI-Augmented Health and Nutrition Intelligence Platform

Disha Katare Ashwini Garkhedkar

Subject area: Science,Engineering and Technology  ·  Area of research: Artificial Intelligence, Data Science

DOI: https://doi.org/10.64388/IREV9I11-1718340

Abstract

The growing prevalence of lifestyle-related non-communicable diseases has created an urgent need for intelligent, personalised digital health tools. Conventional dietary applications provide only static calorie counters and population-average recommendations, failing to adapt to individual physiology, goals, or evolving health context. We present NutriAI Pro, a full-stack AI-augmented nutrition intelligence platform that integrates a React/Vite single-page frontend, an Express.js middleware server, Firebase cloud services, and the Google Gemini 1.5 Pro multimodal large language model. The system delivers four intelligent modules: an AI health coach conditioned on the user's biometric profile, a Food Vision pipeline that estimates caloric and macronutrient content from meal photographs, a generative weekly meal planner, and a predictive health analytics dashboard built on the Mifflin–St Jeor equation and a composite Health Optimisation Score. Functional evaluation demonstrates a 93% food-recognition accuracy, sub-second authentication, AI coaching latency of 1.8–3.2 seconds, and a 100% pass rate across the defined test suite. The work contributes a replicable engineering blueprint for AI-augmented consumer health platforms.

Keywords

Nutritional Intelligence, Large Language Models, Generative AI, Health Analytics, React, Firebase, Google Gemini, Full-Stack Development, Personalised Wellness, Multimodal AI.

References

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[12] Yanai, K., & Kawano, Y. (2015). Food image recognition using deep convolutional network with pre-training and fine-tuning. IEEE ICMEW, 1–6.

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How to cite this paper

Disha Katare, Ashwini Garkhedkar "NutriAI Pro – An AI-Augmented Health and Nutrition Intelligence Platform" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 4561-4568 https://doi.org/10.64388/IREV9I11-1718340
Disha Katare, Ashwini Garkhedkar "NutriAI Pro – An AI-Augmented Health and Nutrition Intelligence Platform" Iconic Research And Engineering Journals, vol. 9, no. 11, May. 2026, doi: https://doi.org/10.64388/IREV9I11-1718340
Disha Katare, Ashwini Garkhedkar (2026). NutriAI Pro – An AI-Augmented Health and Nutrition Intelligence Platform. Iconic Research And Engineering Journals, 9(11). doi: https://doi.org/10.64388/IREV9I11-1718340
Disha Katare, Ashwini Garkhedkar "NutriAI Pro – An AI-Augmented Health and Nutrition Intelligence Platform" Iconic Research And Engineering Journals, vol. 9, no. 11, May. 2026. Crossref, https://doi.org/10.64388/IREV9I11-1718340
@article{1718340,
      author = {Disha Katare, Ashwini Garkhedkar},
      title = {NutriAI Pro – An AI-Augmented Health and Nutrition Intelligence Platform},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {4561-4568},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718340.pdf},
      abstract = {The growing prevalence of lifestyle-related non-communicable diseases has created an urgent need for intelligent, personalised digital health tools. Conventional dietary applications provide only static calorie counters and population-average recommendations, failing to adapt to individual physiology, goals, or evolving health context. We present NutriAI Pro, a full-stack AI-augmented nutrition intelligence platform that integrates a React/Vite single-page frontend, an Express.js middleware server, Firebase cloud services, and the Google Gemini 1.5 Pro multimodal large language model. The system delivers four intelligent modules: an AI health coach conditioned on the user's biometric profile, a Food Vision pipeline that estimates caloric and macronutrient content from meal photographs, a generative weekly meal planner, and a predictive health analytics dashboard built on the Mifflin–St Jeor equation and a composite Health Optimisation Score. Functional evaluation demonstrates a 93% food-recognition accuracy, sub-second authentication, AI coaching latency of 1.8–3.2 seconds, and a 100% pass rate across the defined test suite. The work contributes a replicable engineering blueprint for AI-augmented consumer health platforms.},
      keywords = {Nutritional Intelligence, Large Language Models, Generative AI, Health Analytics, React, Firebase, Google Gemini, Full-Stack Development, Personalised Wellness, Multimodal AI.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1718340}
  }