Dogra Siddhant, Silva Ezequiel Zeke, Rajpurkar Pranav
Department of Radiology, New York University Langone Health, New York, NY, USA.
South Texas Radiology, San Antonio, TX, USA.
NPJ Digit Med. 2024 Dec 2;7(1):350. doi: 10.1038/s41746-024-01352-w.
We argue that generalist radiology artificial intelligence (GRAI) challenges current healthcare reimbursement frameworks. Unlike narrow AI tools, GRAI's multi-task capabilities render existing pathways inadequate. This perspective examines key questions surrounding GRAI reimbursement, including issues of coding, valuation, and coverage policies. We aim to catalyze dialogue among stakeholders about how reimbursement might evolve to accommodate GRAI, potentially influencing AI reimbursement strategies in radiology and beyond.
我们认为，通用放射学人工智能（GRAI）对当前的医疗保健报销框架构成了挑战。与狭义的人工智能工具不同，GRAI的多任务能力使现有的报销途径变得不足。这一观点审视了围绕GRAI报销的关键问题，包括编码、估值和覆盖政策等问题。我们旨在促进利益相关者之间就报销如何演变以适应GRAI展开对话，这可能会影响放射学及其他领域的人工智能报销策略。
