Artificial Intelligence Integrated in Nutrition: A Mini Review on Artificial Intelligence in Estimating and Reducing Dietary Sodium Intake.

Abstract

Excessive sodium intake remains a critical global health concern, significantly contributing to cardiovascular diseases and associated mortality. Traditional sodium intake reduction strategies have faced limitations in accuracy, compliance, and scalability. This review explores the transformative role of artificial intelligence (AI) in sodium intake estimation and reduction, marking a paradigm shift in dietary management. AI-driven innovations-ranging from image-based nutrient analysis to machine learning models-offer real-time, personalized dietary assessments that surpass conventional methods in precision and user engagement. This review uniquely consolidates emerging AI applications, including smartphone-based sodium tracking, predictive analytics, and AIenhanced behavioral modification tools, highlighting their potential to revolutionize dietary interventions. AI-powered solutions, such as image recognition for food composition and intelligent dietary coaching, have demonstrated enhanced accuracy in sodium monitoring and behavioral adaptation. However, variations in efficacy necessitate further refinement and integration into public health frameworks. By systematically evaluating AI's capabilities and limitations in sodium management, this review underscores its potential to bridge the gap between theoretical advancements and realworld implementation. The novelty of this work lies in its comprehensive synthesis of AI applications, presenting a future-oriented perspective on how AI-driven technologies can personalize and optimize sodium intake regulation. Future research should focus on improving AI model accuracy, user engagement, and clinical applicability for widespread adoption.