The report includes:
- Impact analysis of artificial intelligence (AI) on the clinical nutrition market
- Case studies of the successful integration of AI and machine learning in infant nutrition, enteral nutrition and parenteral nutrition
- Highlights of AI adoption trends across major geographical regions, as well as disruptions caused by AI in infant, geriatrics, child and adults users
- Review of the impact of AI on various stages of the clinical nutrition market's value chain, along with a supply chain analysis
- Competitive landscape featuring key strategic initiatives, market spending and an investment outlook
Table of Contents
Chapter 1 Key Use Cases for AI Implementation- Overview
- Key Use Cases
- Use Case 1: Precision Enteral and Parenteral Nutrition
- Use Case 2: Medical Nutrition Therapy (MNT) for Chronic Diseases
- Overview
- Adoption Trends by Region
- Adoption Trends in North America
- Adoption Trends in Europe
- Adoption Trends in Asia-Pacific
- Adoption Trends in the Rest of the World
- Overview
- Impact of AI on Key Industry Verticals
- Enteral Nutrition
- Parenteral Nutrition
- Infant Nutrition
- Impact of AI on Various Stages of the Value Chain
- R&D and Ingredient Discovery
- Manufacturing and Quality Control
- Packaging
- Wholesale Distributors & Repackages
- Overview
- Market Spending and Forecasting Analysis
- Key Spending by Clinical Nutrition Companies on AI
- Venture Capital Investment in AI Focused on Relevant Technologies and Products
- Investments by Major Companies in AI
- Abbreviations Used in the Report
Table 1: Global Investment in Clinical Nutrition Market, 2025
Table 2: Key Spending by Companies on AI, 2025
Table 3: Key Spending by Companies on AI, 2023-2026
Table 4: Abbreviations Used in This Report
List of Figures
Figure 1: Value Chain for the Global Clinical Nutrition Market

