NutriSafe is a research-driven platform that aims to provide personalised dietary guidance for people living with multiple chronic conditions. It is powered by Multi-Risk Precision Nutrition AI (MRPN-AI) and supported by intelligent computational methods to help users better understand food-related health risks.
MRPN-AI stands for Multi-Risk Precision Nutrition AI. It is a new AI-driven approach designed to assess how foods may affect multiple chronic diseases at the same time, rather than focusing on only one condition.
Many existing nutrition apps are built around single-condition advice, which means they often struggle when a person has more than one chronic illness, such as diabetes and kidney disease together. NutriSafe aims to address this gap by providing more integrated, personalised, and culturally relevant dietary support.
NutriSafe is designed specifically for multi-condition dietary decision support. It combines AI, data science, nutrition knowledge, conflict resolution, personalisation, and explainability into one research platform. It also places special emphasis on South Asian foods, which are often underrepresented in mainstream nutrition apps.
The project is primarily focused on chronic conditions such as:
The long-term goal is to support more complex, multi-condition nutrition scenarios.
“Multi-risk” nutrition means looking at how a food may affect more than one health condition at the same time. For example, a food may be acceptable for one condition but risky for another. NutriSafe is designed to assess these overlapping risks and provide more balanced dietary guidance.
The main goal is to build a scalable and intelligent nutrition support system that can:
In this project, Agentic AI refers to an intelligent system that helps coordinate and manage different parts of MRPN-AI. Instead of acting like a simple chatbot or single model, it works as a smart decision-support layer that can reason across multiple inputs, generate personalised guidance, and explain recommendations clearly.
The Agentic AI orchestration layer is the intelligent coordination framework that brings together the different MRPN-AI components such as risk scoring, personalisation, explainability, and conflict resolution, so they can work together in a structured and adaptive way. This helps the system provide context-aware, personalised, and explainable dietary recommendations.
NutriSafe is being designed to combine:
to generate recommendations that are better suited to an individual’s health and dietary context.
Explainable decision support means the system will not only provide a recommendation, but also help the user understand why that recommendation was made. For example, it may explain whether a food is limited due to sugar, sodium, potassium, fat, or another nutritional factor relevant to a person’s conditions.
Yes. While the project begins with a focus on South Asian foods, the long-term goal is to support foods and dietary patterns from around the world through the use of global nutrition databases and culturally relevant data.
The project draws on several trusted data sources, including:
These sources support the scientific and cultural relevance of the models.
The research is expected to use trusted food composition resources such as:
These databases help provide accurate nutrient data for building robust nutrition models.
The project brings together expertise from multiple areas, including:
This multidisciplinary approach is essential for building a practical and scientifically grounded platform.
NutriSafe is currently being developed as a research initiative. The project is focused on building and validating the scientific, technical, and user-centred foundations of the platform before wider public deployment.
No. NutriSafe is an educational and decision-support research platform. It is designed to help users better understand food-related health risks, but it is not a substitute for professional medical advice, diagnosis, or treatment.
No. Users should always consult a qualified healthcare professional before making significant changes to their diet, medication, or treatment plan. NutriSafe is intended to support understanding and decision-making, not replace medical care.
The project aims to build a cross-platform mobile application that works on both iOS and Android. The goal is to make personalised nutrition support more accessible, practical, and user-friendly.
The long-term vision is to establish Multi-Risk Precision Nutrition AI (MRPN-AI) as a new field of study and to develop intelligent systems that can support safer, more personalised, and culturally relevant nutrition guidance for people living with multiple chronic conditions.
