Publication Details
Abstract
In the context of ongoing digital transformation, healthcare systems in transition economies face the dual challenge of modernizing diagnostic capabilities and ensuring accessibility to high-quality services. This study explores an innovative approach to gastrointestinal diagnostics through the integration of AI-driven saliva-based systems, focusing on the example of Uzbekistan. The research analyzes the effectiveness of the "Saliva" digital device, which utilizes machine learning algorithms—particularly the Random Forest classifier—for the early detection of gastrointestinal diseases. Through empirical testing, economic modeling, and scenario-based simulations, the study demonstrates the potential of such technology to improve diagnostic accuracy, reduce costs, and increase coverage in underserved regions. The findings highlight how digital tools, when strategically embedded into national healthcare systems, can drive institutional efficiency, preventive care, and sustainable medical service delivery in transition economies.