INTELLIGENT MONITORING SYSTEMS IN OUTPATIENT GYNECOLOGY: GLOBAL PRACTICES, LOCALIZATION CHALLENGES, AND AI-ASSISTANT FRAMEWORK DESIGN FOR UZBEKISTAN
Abstract
Improving the quality of outpatient gynecological care relies heavily on continuous patient monitoring, which is often unfeasible within traditional healthcare models. While the deployment of AI-driven algorithms and chatbots offers a promising alternative, its realization in low- and middle-income countries (LMICs) faces significant hurdles, including domain shift, linguistic barriers, and socio-cultural constraints. This study aims to synthesize global insights into digital reproductive health interventions and outline a conceptual framework for an AI-powered post-consultation recommendation system tailored to the specific context of the Republic of Uzbekistan. Based on a comprehensive literature review (2019–2025) and an evaluation of Uzbekistan's current healthcare regulatory environment, the authors demonstrate that global turnkey solutions fail to account for local epidemiological patterns and societal stigmatization. Consequently, this paper introduces a modular architecture for a hybrid "patient–AI–clinician" system leveraging the Human-in-the-loop paradigm. This system is anticipated to minimize patient anxiety and optimize treatment adherence. The proposed model establishes a robust and culturally competent foundation for digital medical interventions capable of seamless integration into Uzbekistan's national digital health infrastructure.
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