Artificial Intelligence in Nutrition Management: How Data-Driven Tools Can Improve Diet Planning, Patient Support, and Public Health

DOI 10.65326/u7y566766

Abstract

Nutrition management has become increasingly complex because of the growth of diet-related diseases, unequal access to professional dietary support, and the rising demand for personalized care in health systems. Artificial intelligence can enhance the support received by patients and communities by transforming fragmented dietary, clinical, behavioral, and population data into more flexible forms of decision support. This paper develops a qualitative conceptual analysis of artificial intelligence in nutrition management through the lens of stakeholder theory. It argues that the value of data-driven nutrition tools depends not only on technical performance, but also on their ability to address the needs of patients, dietitians, clinicians, health services, public health authorities, and affected communities. The paper identifies three main areas of contribution: personalized diet planning, continuous patient engagement, and the integration of nutrition data for public health decision-making. It also examines important weaknesses, including the possible loss of professional judgment, poor or over-standardized dietary advice, algorithmic bias, and weak data governance. The paper argues that artificial intelligence can strengthen nutrition management when it supports human expertise rather than replaces it. Responsible implementation requires culturally responsive design, transparent governance, professional oversight, and diverse models of collaboration.