Leveraging Artificial Intelligence to Simulate Public Health Policy Impacts for Obesity Prevention in Saudi Arabia

Abstract

Abstract Background: Obesity poses one of the most significant public health challenges globally, with Saudi Arabia facing particularly high prevalence rates. Traditional public health strategies often lack the precision and scalability required to address the complex and multifaceted drivers of obesity. In Saudi Arabia, where lifestyle-related diseases such as obesity and diabetes are prevalent, artificial intelligence (AI) can provide data-driven, scalable, and efficient solutions. Policymakers need effective tools to predict the impacts of interventions to reduce obesity prevalence. AI has emerged as a transformative tool for simulating and evaluating public health policies. Research Question: How can AI-driven simulation models be developed and utilized to evaluate the effectiveness of obesity prevention policies and guide resource allocation in Saudi Arabia? Methods: We will explore the novel framework for the integration of AI techniques such as machine learning and natural language processing (NLP) to develop a policy impact simulation model. The impact of policy changes will be investigated using specific case studies. Simulations will explore diverse policy interventions, including public awareness campaigns, nutritional regulations, and urban planning for physical activity promotion. Contributions to the Field: The policy impact simulation model will offer a glimpse of the real-time outcomes of policy implementations to prevent obesity. It will be useful to examine case studies, challenges, and potential results, offering a roadmap with actionable insight for policymakers. The proposed model is expected to inform policy decisions by providing simulated insights into the effectiveness of obesity prevention strategies, enabling data-driven prioritization of interventions, and supporting long-term planning for public health improvement in Saudi Arabia.

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