Evaluating the value of AI in lung imaging: a comprehensive review and evidence gap analysis from the healthcare authorities' and reimbursement perspective

Abstract

<bold>Introduction:</bold> The increasing role of AI in lung imaging collides with the limited availability of official reimbursement policies on its use. Acknowledging the pivotal role reimbursement plays in technology adoption, this review explores the perspectives of healthcare authorities and reimbursement bodies on the value of AI. The primary objective is to determine conditions under which AI usage qualifies for reimbursement. The study also identifies evidence gaps from the reimbursement standpoint, offering opportunities for the scientific community to contribute and ensure appropriate compensation for AI use. <bold>Methods:</bold> A systematic review of literature across databases and examinations of payment policies and clinical guidance documents on the use of AI for lung imaging were performed. The requirements for AI recommendation and reimbursement stipulated by payers and health authorities were benchmarked against published evidence. <bold>Results:</bold> The synthesis of payment policies and clinical guidance documents highlighted that the critical determinant for reimbursement of AI applications by payers and health authorities is the AI's ability to provide novel diagnostic information rather than simply duplicating physician work and its subsequent impact on downstream healthcare cost and resource utilization. <bold>Conclusion:</bold> Reimbursement typically covers physician work and resource utilization. In the context of lung imaging AI has the potential to influence both aspects. AI solutions likely only qualify for additional reimbursement in case they are able to generate new diagnostic information, such as malignancy scores of pulmonary nodules.

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