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Related Experiment Video

Updated: May 19, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

An explainable graph retrieval augmented generation framework for personalized nutrition recommendation.

Varrsan Dindukurthi1, Dhruv Jain1, Anubhava Tripathi1

  • 1School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Frontiers in Artificial Intelligence
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a graph-based AI for personalized nutrition, improving dietary planning for non-communicable diseases by grounding recommendations in knowledge graphs and demographic data.

Keywords:
AI-powered nutritionNeo4jcosine similarityculturally relevant dietexplainable AIgraph retrieval-augmented generation (GraphRAG)knowledge graphlarge language models (LLMs)

Related Experiment Videos

Last Updated: May 19, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Artificial Intelligence in Nutrition
  • Graph-based Decision Support Systems
  • Personalized Dietary Planning

Background:

  • Current AI nutrition systems struggle with knowledge grounding, demographic sensitivity, and explainability, especially in diverse cultural contexts like India.
  • Existing approaches often fail to integrate clinical dietary needs with traditional meal patterns, limiting their effectiveness.

Purpose of the Study:

  • To develop a graph-centric decision-support framework using Graph Retrieval-Augmented Generation (Graph-RAG) for AI-based nutrition planning.
  • To enhance demographic sensitivity and explainability in nutrition recommendations for non-communicable diseases.

Main Methods:

  • A Neo4j knowledge graph was constructed to model relationships between diseases, nutrients, foods, and demographic-specific Recommended Dietary Allowances (RDA).
  • A semantic Extract, Transform, Load (ETL) pipeline integrated diverse datasets, resolving inconsistencies using embedding-based alignment.
  • Nutrient requirements were retrieved and matched with food profiles using a cosine similarity algorithm, with a language model formatting graph-validated outputs.

Main Results:

  • The framework demonstrated improved ranking consistency and alignment with nutrient requirements across case studies (anemia, hypertension, diabetes).
  • Enhanced demographic sensitivity and reduced nutrient-dominance bias were observed compared to baseline methods.
  • RDA-based normalization was found to significantly improve nutritional balance.

Conclusions:

  • Combining graph reasoning with constrained language generation offers transparent, knowledge-guided nutrition recommendations, improving interpretability.
  • The system acts as a decision-support tool, mitigating risks associated with unconstrained AI models.
  • Future research will focus on uncertainty modeling, dataset expansion, and expert validation for clinical application.