Artificial Intelligence in Clinical Nutrition: Bridging Data Analytics and Nutritional Care

Area of Science:

• Clinical Nutrition
• Artificial Intelligence in Healthcare
• Medical Informatics

Background:

• Nutrition education and practice face significant challenges.
• Personalized nutrition interventions require advanced decision-making support.
• A knowledge gap exists in delivering real-time, evidence-based nutritional insights.

Purpose of the Study:

• To explore how artificial intelligence (AI) can advance clinical nutrition.
• To address challenges in nutrition education and practice using AI.
• To highlight AI's role in personalized nutrition interventions and knowledge dissemination.

Main Methods:

• Review of AI applications in clinical nutrition.
• Analysis of machine learning and natural language processing in nutritional outcome prediction.
• Exploration of generative AI for clinical decision support.

Main Results:

• AI shows promise in predicting nutritional outcomes and complications like malnutrition.
• AI efficiently processes large datasets to identify risk factors and support clinicians.
• AI can personalize educational content, improving accessibility of nutritional concepts.

Conclusions:

• AI has multiple potential applications in nutrition, including personalized interventions and education.
• AI can provide real-time, evidence-based insights to healthcare professionals.
• Further research is required to assess AI's accuracy, accessibility, and ethical implications in nutrition.