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Applications of artificial intelligence in the management of childhood obesity
1Department of Family and Community Medicine, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Insights
Artificial intelligence (AI) shows promise in tackling childhood obesity. Machine learning techniques enhance current prevention and treatment strategies for pediatric obesity management.
Area of Science:
- Public Health
- Medical Informatics
- Pediatrics
Background:
- Childhood obesity is a growing public health concern.
- It has long-term health consequences into adulthood.
- Increased susceptibility to chronic diseases is a key implication.
Purpose of the Study:
- To review artificial intelligence (AI) applications in pediatric obesity.
- To explore AI's role in prevention and treatment.
- To assess AI's potential to enhance traditional methods.
Main Methods:
- Comprehensive literature review.
- Examination of AI and machine learning integration.
- Analysis of childhood obesity management strategies.
Main Results:
- AI is strongly endorsed for childhood obesity intervention.
- Machine learning techniques show efficacy.
- AI augments current therapeutic and preventive approaches.
Conclusions:
- AI offers a novel approach to pediatric obesity management.
- AI has transformative potential in this field.
- Continued research and innovation in AI for obesity are advocated.
Background:
Childhood obesity has emerged as a significant public health challenge, with long-term implications that often extend into adulthood, increasing the susceptibility to chronic health conditions.
Objective:
The objective of this review is to elucidate the applications of artificial intelligence (AI) in the prevention and treatment of pediatric obesity, emphasizing its potential to complement and enhance traditional management methods.
Methods:
We undertook a comprehensive examination of existing literature to understand the integration of machine learning and other AI techniques in childhood obesity management strategies.
Results:
The findings from numerous studies suggest a strong endorsement for AI's role in addressing childhood obesity. Particularly, machine learning techniques have shown considerable efficacy in augmenting current therapeutic and preventive approaches.
Conclusion:
The intersection of AI with conventional obesity management practices presents a novel and promising approach to fortify interventions targeting pediatric obesity. This review accentuates the transformative capacity of AI, thereby advocating for continued research and innovation in this rapidly evolving domain.
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