Lobig Franziska, Subramanian Dhinagar, Blankenburg Michael, Sharma Ankur, Variyar Archana, Butler Oisin
Bayer AG, Berlin, Germany.
Qlaar Limited, Oxford, United Kingdom.
NPJ Digit Med. 2023 Jun 23;6(1):117. doi: 10.1038/s41746-023-00861-4.
Artificial Intelligence-supported digital applications (AI applications) are expected to transform radiology. However, providers need the motivation and incentives to adopt these technologies. For some radiology AI applications, the benefits of the application itself may sufficiently serve as the incentive. For others, payers may have to consider reimbursing the AI application separate from the cost of the underlying imaging studies. In such circumstances, it is important for payers to develop a clear set of criteria to decide which AI applications should be paid for separately. In this article, we propose a framework to help serve as a guide for payers aiming to establish such criteria and for technology vendors developing radiology AI applications. As a rule of thumb, we propose that radiology AI applications with a clinical utility must be reimbursed separately provided they have supporting evidence that the improved diagnostic performance leads to improved outcomes from a societal standpoint, or if such improved outcomes can reasonably be anticipated based on the clinical utility offered.
人工智能支持的数字应用程序（人工智能应用）有望变革放射学。然而，供应商需要有动力和激励措施来采用这些技术。对于一些放射学人工智能应用，应用本身的益处可能足以作为激励因素。对于其他应用，支付方可能不得不考虑将人工智能应用的费用与基础影像检查的费用分开报销。在这种情况下，支付方制定一套明确的标准以决定哪些人工智能应用应单独付费非常重要。在本文中，我们提出一个框架，旨在为旨在建立此类标准的支付方以及开发放射学人工智能应用的技术供应商提供指导。根据经验法则，我们建议具有临床实用性的放射学人工智能应用必须单独报销，前提是它们有支持性证据表明从社会角度来看，诊断性能的提高会带来更好的结果，或者基于所提供的临床实用性可以合理预期会有这样的改善结果。
