Abstract
Issues of reimbursement for medical care are gaining increasing importance due to the expansion of national guarantee programs, an aging population, and the prolonged treatment of costly diseases. This document systematizes international experiences of incorporating artificial intelligence technologies into health insurance systems and proposes best practices.The aim: The integration of artificial intelligence (AI) in healthcare systems has catalyzed the evolution of various payment models for AI-driven medical services. This article aims to present a comprehensive review of the diverse payment models implemented across different countries, with a focus on promoting technological advancement, maintaining healthcare system standards, and ensuring guaranteed clinical outcomes.Methods. This study employs a systematic review methodology, analyzing existing literature on AI-driven healthcare service payment models. The analysis includes categorization of these models into three main types with potential hybrid variations. Sources were selected based on relevance, peer-review status, and contribution to the understanding of AI reimbursement in healthcare. Data was extracted and synthesized to identify patterns and gaps in current payment practices.Results. The review identifies three primary categories of AI-enabled healthcare service payment models: fee-for-service, bundled payments, and value-based payments. Hybrid models combining elements of these categories are also discussed. Key findings highlight the strengths and limitations of each model in terms of promoting innovation and achieving clinical outcomes. Despite the rapid advancements in AI technologies, the study reveals a scarcity of systematic research comparing the effectiveness of different reimbursement schemes.Conclusion. This article underscores the critical need for further studies to navigate the evolving technological landscape and international regulatory frameworks. Significant directions in the development of reimbursement models for AI in healthcare are explored, including the adaptation of Software as a Service (SaaS) payment models, the implementation of anticipatory funding mechanisms, and the differentiation between AI registration and its inclusion in insurance reimbursement programs as a distinct service. These insights are essential for policymakers, healthcare providers, and technology developers to optimize payment strategies and enhance the integration of AI in healthcare.Keywords: artificial intelligence, healthcare, payment models, reimbursement, technology, clinical outcomes, regulatory activities.