Research Behind HealthPeak
Built on peer-reviewed research in AI, food systems, and digital health — federated learning, explainable AI, knowledge graphs, and personalized nutrition.
From Research to Preventive Health Intelligence
HealthPeak emerged from research exploring how artificial intelligence can transform food systems, digital health, and personalized nutrition.
Over the past years, research in this space has investigated topics including federated learning, explainable AI, semantic interoperability, and AI-driven nutrition systems.
These research streams converge into HealthPeak — a platform designed to support preventive health through evidence-based guidance.
Scientific Foundations
Three research pillars underpin the HealthPeak platform
Trustworthy AI Infrastructure
Research in this area focuses on building trustworthy AI systems capable of integrating data across organizations while preserving privacy, transparency, and interoperability.
Key themes
- Federated learning
- Data governance
- Semantic interoperability
- Explainable AI
Publications
- Applying federated learning to combat food fraud in food supply chains — npj Science of Food (2023)
- Agricultural data privacy: Emerging platforms & strategies — Food and Humanity (2025)
- The role and applications of semantic interoperability tools and explainable AI in the development of smart food systems — Intelligent Systems with Applications (2025)
- Food fraud detection using explainable artificial intelligence — Expert Systems (2025)
→ Contributes to HealthPeak data infrastructure layer
AI-Driven Food & Nutrition Intelligence
This research stream explores how artificial intelligence can support personalized nutrition and sustainable food systems.
Key themes
- Personalized nutrition
- Digital health
- Sustainable food recommendations
- AI decision support
→ Contributes to NutriGreen module inside HealthPeak
Knowledge Graphs & AI Reasoning
This research investigates how knowledge graphs and artificial intelligence can integrate scientific knowledge across domains and support reasoning over complex health information.
Key themes
- AI knowledge graphs
- Evidence reasoning
- Intelligent recommendation systems
- Scientific knowledge integration
Publications
→ Contributes to HealthPeak reasoning engine and knowledge graph architecture
Evidence-Driven Intelligence
HealthPeak integrates scientific evidence directly into its reasoning system. The platform combines large language models, retrieval-augmented generation (RAG), and knowledge graphs to ensure that recommendations are grounded in scientific research.
Scientific Publications
Peer-reviewed research across AI, nutrition, and digital health
Knowledge Graph
Structured representation of scientific evidence and relationships
Evidence Retrieval
RAG-based retrieval of relevant research for each query
AI Reasoning Engine
LLM-powered synthesis and reasoning over retrieved evidence
Personalized Guidance
Tailored recommendations grounded in scientific literature
How Research Powers HealthPeak
Key Publications
AI-driven personalized nutrition: RAG-based digital health solution for obesity and type 2 diabetes
PLOS Digital Health
20 citationsApplying federated learning to combat food fraud in food supply chains
npj Science of Food
24 citationsToward Preventive Digital Health
HealthPeak represents the next step in translating scientific research into real-world health intelligence systems. By combining large language models, knowledge graphs, and evidence-based nutrition science, HealthPeak aims to provide accessible preventive health guidance for individuals worldwide.
Collaborate With Us
We welcome collaboration from researchers, universities, healthcare organizations, industry partners, and investors.