APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN METABOLIC HEALTH
DOI:
https://doi.org/10.56238/levv14n32-039Keywords:
Artificial Intelligence, Metabolism, Prediction, Chronic Diseases, Digital HealthAbstract
Artificial intelligence has expanded the possibilities for interpreting metabolic processes by enabling large volumes of clinical, nutritional and behavioral data to be examined in an integrated manner, supporting the identification of patterns that contribute to risk anticipation, the refinement of preventive strategies and the development of clinical approaches aligned with the physiological singularities of each individual, consolidating a model of care based on continuous and in-depth reading of metabolic variations and reinforcing interventions grounded in robust information that reflects biological and social realities observed in different health contexts. This study describes and discusses the application of artificial intelligence in metabolic monitoring, highlighting its ability to enhance practices related to the prediction of chronic diseases and the monitoring of clinical indicators that directly influence human metabolic evolution, allowing the development of more personalized strategies adapted to contemporary clinical demands, contributing to the expansion of scientific knowledge and to the construction of care models that integrate technology, predictive analysis and comprehensive understanding of the patient as central elements in promoting more consistent and effective clinical outcomes.
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