Robert F. Kennedy Jr. has been advocating for more protein in American diets, with recent government dietary guidelines suggesting an increase in protein intake. However, the Grok-powered food website, Realfood.gov, offers advice that contradicts these recommendations, especially around protein consumption. While the guidelines now recommend 1.2 to 1.6 grams of protein per kilogram of body weight per day, Grok, powered by Elon Musk's AI tool, suggests a lower intake of 0.8 grams per kilogram per day for most individuals. The website aligns with the administration's findings that protein deficiency in the U.S. is rare but still encourages higher protein intake for those with specific needs, like regular exercisers. Additionally, Grok lists plant-based proteins, seafood, and lean poultry as healthier sources of protein, in contrast to Kennedy’s emphasis on red meat. Critics, including dietitians, caution against relying on Grok for detailed nutritional advice, as AI-generated information can be inaccurate or misleading. Despite the mixed messaging, the article highlights the need for a nuanced approach to protein intake that reflects individual health goals and activity levels.

Nutrition plays a pivotal role in the prevention and management of gastrointestinal and hepatic diseases, yet dietary guidance remains generic, limiting its effectiveness. Conditions such as inflammatory bowel disease, irritable bowel syndrome, metabolic dysfunction-associated steatotic liver disease, celiac disease, and gastroesophageal reflux disease are significantly influenced by dietary factors. Personalized nutrition has emerged as a promising strategy to tailor interventions, but conventional approaches fail to account for individual metabolic, genetic, and microbiome variability, limiting their clinical impact. The rapid rise of artificial intelligence (AI) has transformed precision nutrition by integrating genomics, microbiome profiles, metabolic markers, and real-time dietary tracking to generate individualized recommendations. AI-driven systems are advancing dietary assessment, condition-specific nutrition optimization, and continuous monitoring through tools such as wearable devices and natural language processing-based diet analysis. These innovations hold transformative potential in gastroenterology and hepatology, offering dynamic, patient-specific strategies that may enhance clinical outcomes. However, challenges remain, including the lack of standardized AI-driven protocols, ethical concerns such as bias and data privacy, limited clinical validation, and the underrepresentation of nutrition in many current AI applications. Opportunities for progress include developing federated learning models, expanding real-world validation studies, and designing regulatory and ethical frameworks for safe implementation. This narrative review synthesizes literature published between 2015 and 2025 across five databases, highlighting key applications, limitations, and future directions of AI-driven personalized nutrition in gastroenterology and hepatology. It provides insights into how AI could reshape patient-centered care through more individualized, effective, and scalable dietary strategies.

Nutrition plays a pivotal role in the prevention and management of gastrointestinal and hepatic diseases, yet dietary guidance remains generic, limiting its effectiveness. Conditions such as inflammatory bowel disease, irritable bowel syndrome, metabolic dysfunction-associated steatotic liver disease, celiac disease, and gastroesophageal reflux disease are significantly influenced by dietary factors. Personalized nutrition has emerged as a promising strategy to tailor interventions, but conventional approaches fail to account for individual metabolic, genetic, and microbiome variability, limiting their clinical impact. The rapid rise of artificial intelligence (AI) has transformed precision nutrition by integrating genomics, microbiome profiles, metabolic markers, and real-time dietary tracking to generate individualized recommendations. AI-driven systems are advancing dietary assessment, condition-specific nutrition optimization, and continuous monitoring through tools such as wearable devices and natural language processing-based diet analysis. These innovations hold transformative potential in gastroenterology and hepatology, offering dynamic, patient-specific strategies that may enhance clinical outcomes. However, challenges remain, including the lack of standardized AI-driven protocols, ethical concerns such as bias and data privacy, limited clinical validation, and the underrepresentation of nutrition in many current AI applications. Opportunities for progress include developing federated learning models, expanding real-world validation studies, and designing regulatory and ethical frameworks for safe implementation. This narrative review synthesizes literature published between 2015 and 2025 across five databases, highlighting key applications, limitations, and future directions of AI-driven personalized nutrition in gastroenterology and hepatology. It provides insights into how AI could reshape patient-centered care through more individualized, effective, and scalable dietary strategies.