Valuing diagnostic AI: a structured reimbursement model for learning healthcare systems

Abstract

AI-based diagnostic decision support systems (DDSS) play a growing role in modern healthcare and hold considerable promise in contributing to learning healthcare systems, settings in which clinical practice and data-driven insights are closely integrated. DDSSs are increasingly used in radiology, cardiology, laboratory diagnostics and pathology, where they assist clinicians in interpreting complex data, standardized decision making, and improving outcomes. However, despite their clinical relevance, such systems remain difficult to evaluate and integrate within current reimbursement structures. Traditional key performance indicators (KPIs), such as case costs, turnaround times, or documentation completeness, are insufficient to capture the nuanced contributions of AI systems to clinical value and learning cycles. As a result, DDSS often operate outside established reimbursement logics, limiting their broader adoption and sustainability. This article addresses the economic and regulatory disconnect between the measurable value of AI-assisted diagnostics and their lack of inclusion in existing reimbursement frameworks. It introduces a structured, point-based reimbursement model specifically designed to support the integration of DDSS into real-world payment systems, using the German and American coding systems as reference models. By linking reimbursement levels with diagnostic complexity and degree of contribution from AI, the proposed framework promotes fair compensation, encourages meaningful use, and supports responsible clinical deployment. We document a multi-criteria point calibration which is anchored to existing codes. In addition, the model fosters an auditable feedback-driven structure that could support adaptive payment in learning healthcare systems. In this way, the framework is not merely a pricing tool; it also serves as a governance mechanism that aligns economic incentives with ethical, clinical, and operational priorities in AI adoption. It contributes to the realization of a learning healthcare system by enabling continuous refinement, transparent valuation, and sustainable implementation of AI-driven diagnostics.

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