Course Overview
AI in Nutrition and Dietetics is a specialized 3-weeks online course designed to empower dietitians, nutritionists, healthcare professionals, and tech-savvy learners to integrate artificial intelligence into nutrition science. The course focuses on AI-powered tools for personalized diet planning, dietary data analysis, food tracking technologies, and predictive health risk assessments. Learners will explore real-world use cases, emerging AI trends, and ethical concerns surrounding AI in healthcare and nutrition practices.
Program Objectives
- Bridge AI and Nutrition Science: Build a strong foundation in the intersection of AI technology and evidence-based dietary science.
- Develop Data Literacy: Understand how to collect, process, and interpret food and health data to train and utilize AI models.
- Design Personalized Diet Plans: Learn to work with AI tools that consider biomarkers, lifestyle, allergies, and goals for customized nutrition.
- Predict and Prevent Health Risks: Use predictive modeling to identify and prevent diet-related health conditions like obesity and Type 2 diabetes.
- Explore Smart Nutrition Apps: Get hands-on experience with leading AI tools and apps that revolutionize food tracking and coaching.
- Foster Responsible AI Usage: Address ethical implications and data governance for AI in the health and nutrition ecosystem.
Program Structure
- Week 1 – Foundations of AI in Nutrition:
- Introduction to Artificial Intelligence and Machine Learning fundamentals
- Overview of nutrition science and current digital transformation trends
- Understanding food databases, nutritional biomarkers, and structured dietary data
- Key tools: MyFitnessPal, USDA FoodData Central, NutriSurvey, and open-access APIs
- Week 2 – AI Applications in Diet Planning and Food Tracking:
- How AI builds personalized meal plans using medical, lifestyle, and genetic inputs
- NLP and image recognition in food tracking and meal logging apps
- Real-time feedback systems for nutrition coaching and smart assistants
- Case studies: AI in sports nutrition, pediatric nutrition, and metabolic disorders
- Week 3 – Predictive Analytics, Ethics & Implementation:
- Building predictive models for obesity, diabetes, and cardiovascular risk
- Deep learning for microbiome and nutrigenomics interpretation
- Data privacy, consent, and ethical issues in AI-led health personalization
- Hands-on mini-project: Develop a prototype recommendation model using dummy health data
Eligibility
- Nutrition Students and Professionals: BSc/MSc in Nutrition, Dietetics, or Food Science.
- Healthcare Practitioners: Medical professionals, nurses, and clinical researchers interested in AI-based dietary assessment.
- Tech Enthusiasts: Individuals with basic AI/ML knowledge seeking applications in health and wellness.
Learning Outcomes
- Implement AI tools: Use AI-powered platforms for designing diet plans and analyzing food intake.
- Analyze Nutrition Data: Gain the ability to manage and interpret nutritional datasets using machine learning.
- Design Health Interventions: Create evidence-based, tech-enabled nutrition interventions for diverse populations.
- Stay Ahead in Nutrition Tech: Keep up with innovations in smart diet planning, wearable health monitoring, and predictive risk models.
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