A review of AI integration in personalized nutrition: advancements, challenges, and clinical implications

Location

Moultrie, GA

Start Date

7-5-2025 1:00 PM

End Date

7-5-2025 4:00 PM

Description

Introduction Artificial intelligence (AI) is transforming healthcare, including the intersection of nutrition and medicine. Personalized dietary recommendations play a crucial role in managing chronic diseases such as diabetes, cardiovascular conditions, and obesity. AI-driven tools offer the potential to enhance dietary assessments and optimize meal planning based on individual needs. However, ethical and practical challenges remain. This study explores the question: How can AI enhance personalized nutrition recommendations while addressing ethical and practical challenges in clinical applications?

Methods A systematic literature review was conducted, analyzing research from databases such as PubMed and Google Scholar. Studies were selected based on relevance to AI-driven dietary analysis, machine learning in meal planning, and clinical applications. Key themes examined included:

  • AI-driven real-time dietary assessment and its clinical accuracy.

  • Machine learning algorithms for personalized meal planning and adherence monitoring.

  • Implementation of AI-powered applications in clinical nutrition and medicine.

  • Ethical concerns, algorithmic bias, and regulatory barriers in AI-driven nutrition.

Results & Discussion AI-driven personalized nutrition systems show promising capabilities including:

  • Real-time dietary assessment through food tracking applications and wearable sensors.

  • Machine learning-based meal planning to tailor recommendations to an individual’s health profile.

  • Improved patient adherence facilitated by AI-powered apps that provide feedback and reminders.

Despite these advancements, several challenges must be addressed:

  • Algorithmic bias: Many AI models are trained on non-diverse datasets, potentially leading to recommendations that do not accommodate different ethnic or socioeconomic groups.

  • Data privacy and security: The use of personal health data in AI applications raises concerns about confidentiality and protection.

  • Clinical validation and regulatory barriers: The lack of standardized validation methods limits AI’s acceptance in medical practice.

Ethical considerations are central to AI’s integration in clinical practice. Transparency in AI decision-making is necessary to maintain trust, and disparities in AI-generated recommendations must be addressed. Seamless integration into clinical workflows requires interdisciplinary collaboration among healthcare professionals, AI developers, and policymakers.

Conclusion & Future Directions AI has the potential to transform personalized nutrition and chronic disease management. However, ethical concerns and regulatory challenges must be addressed to ensure safe and equitable adoption. Future research should focus on developing inclusive AI models, conducting real-world clinical trials, and integrating AI-driven nutrition recommendations into clinical guidelines. Establishing regulatory oversight will be essential for responsible AI implementation in healthcare.

Embargo Period

5-20-2025

This document is currently not available here.

COinS
 
May 7th, 1:00 PM May 7th, 4:00 PM

A review of AI integration in personalized nutrition: advancements, challenges, and clinical implications

Moultrie, GA

Introduction Artificial intelligence (AI) is transforming healthcare, including the intersection of nutrition and medicine. Personalized dietary recommendations play a crucial role in managing chronic diseases such as diabetes, cardiovascular conditions, and obesity. AI-driven tools offer the potential to enhance dietary assessments and optimize meal planning based on individual needs. However, ethical and practical challenges remain. This study explores the question: How can AI enhance personalized nutrition recommendations while addressing ethical and practical challenges in clinical applications?

Methods A systematic literature review was conducted, analyzing research from databases such as PubMed and Google Scholar. Studies were selected based on relevance to AI-driven dietary analysis, machine learning in meal planning, and clinical applications. Key themes examined included:

  • AI-driven real-time dietary assessment and its clinical accuracy.

  • Machine learning algorithms for personalized meal planning and adherence monitoring.

  • Implementation of AI-powered applications in clinical nutrition and medicine.

  • Ethical concerns, algorithmic bias, and regulatory barriers in AI-driven nutrition.

Results & Discussion AI-driven personalized nutrition systems show promising capabilities including:

  • Real-time dietary assessment through food tracking applications and wearable sensors.

  • Machine learning-based meal planning to tailor recommendations to an individual’s health profile.

  • Improved patient adherence facilitated by AI-powered apps that provide feedback and reminders.

Despite these advancements, several challenges must be addressed:

  • Algorithmic bias: Many AI models are trained on non-diverse datasets, potentially leading to recommendations that do not accommodate different ethnic or socioeconomic groups.

  • Data privacy and security: The use of personal health data in AI applications raises concerns about confidentiality and protection.

  • Clinical validation and regulatory barriers: The lack of standardized validation methods limits AI’s acceptance in medical practice.

Ethical considerations are central to AI’s integration in clinical practice. Transparency in AI decision-making is necessary to maintain trust, and disparities in AI-generated recommendations must be addressed. Seamless integration into clinical workflows requires interdisciplinary collaboration among healthcare professionals, AI developers, and policymakers.

Conclusion & Future Directions AI has the potential to transform personalized nutrition and chronic disease management. However, ethical concerns and regulatory challenges must be addressed to ensure safe and equitable adoption. Future research should focus on developing inclusive AI models, conducting real-world clinical trials, and integrating AI-driven nutrition recommendations into clinical guidelines. Establishing regulatory oversight will be essential for responsible AI implementation in healthcare.