Obesity and the role of artificial intelligence as a prediction tool

Authors

  • Camila A. Campoverde Ministry of Public Health, Quito, Ecuador
  • Emily D. Moyano School of Medicine, PUCE University, Quito, Ecuador
  • Daniela N. Punin School of Medicine, PUCE University, Quito, Ecuador
  • Edison R. Haro Freile School of Medicine, USFQ University, Quito, Ecuador
  • Emily N. Delgado Varela National University of Chimborazo, Riobamba, Ecuador
  • Sarentsa K. Calderon Ministry of Public Health, Quito, Ecuador

DOI:

https://doi.org/10.18203/2320-6012.ijrms20253644

Keywords:

Artificial intelligence, Obesity, Risk prediction, Machine learning, Digital health, Personalized medicine

Abstract

Obesity is one of the leading public health challenges worldwide, with a growing prevalence and a strong association with chronic noncommunicable diseases. Traditional methods for risk assessment and clinical management have shown limitations in addressing the multifactorial complexity of this condition. In this context, artificial intelligence (AI) emerges as an innovative tool capable of enhancing risk prediction and optimizing treatment strategies. This article reviews current AI approaches applied to obesity, ranging from predictive models based on clinical, genomic, and behavioural data to personalized digital interventions in nutrition, physical activity, and therapeutic adherence. Furthermore, the main advantages, limitations, and ethical challenges of clinical implementation are analyzed. Finally, recommendations are provided to guide the responsible integration of AI into medical practice, aiming to advance toward more predictive, preventive, and personalized medicine in obesity management.

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References

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Published

2025-10-30

How to Cite

Campoverde, C. A., Moyano, E. D., Punin, D. N., Freile, E. R. H., Varela, E. N. D., & Calderon, S. K. (2025). Obesity and the role of artificial intelligence as a prediction tool. International Journal of Research in Medical Sciences, 13(11), 5018–5024. https://doi.org/10.18203/2320-6012.ijrms20253644

Issue

Vol. 13 No. 11 (2025): November 2025

Section

Review Articles
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