Speakers - 2025

Biography

The integration of artificial intelligence (AI) into precision medicine has significantly advanced the understanding and management of complex diseases such as obesity. This research utilized AI to synthesize genetic and socioeconomic variables, addressing the need for precise predictive models shown by previous studies that linked obesity traits to socioeconomic status (SES). The objective was to develop and validate a predictive model using AI algorithms and Genomic Structural Equation Modeling to assess obesity risk from comprehensive profiles, including genetic information, age, income, and education levels. This would enable healthcare professionals to make informed decisions about individual obesity risk management.

The methodology involved using Genomic Structural Equation Modeling and iterative simulations to refine obesity risk predictions by isolating genetic variations associated with SES. Advanced AI techniques such as machine learning and neural networks enhanced the predictive accuracy based on specific genetic markers and socioeconomic factors. The model, developed with data from the Genome Wide Association Database (GWAS), proved effective in predicting obesity risks with high accuracy.

The results demonstrated that the AI algorithms could successfully identify key genetic markers that interact with socioeconomic variables to predispose individuals to obesity. This is crucial for the early detection of obesity, enabling timely and effective intervention strategies. The validated model now serves as a vital resource in clinical settings, improving the diagnosis and treatment of obesity through targeted therapies that consider both genetic and socioeconomic dimensions. It not only contributes to global efforts to combat obesity but also underscores the potential of AI to revolutionize healthcare by providing more personalized, predictive, and preventive care.