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Review
. 2024 Dec 2;7(1):350.
doi: 10.1038/s41746-024-01352-w.

Reimbursement in the age of generalist radiology artificial intelligence

Siddhant Dogra  1 , Ezequiel Zeke Silva 3rd  2   3 , Pranav Rajpurkar  4
Affiliations
Review

Reimbursement in the age of generalist radiology artificial intelligence

Siddhant Dogra et al. NPJ Digit Med. .

Abstract

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.

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Conflict of interest statement

Competing interests: P.R. is co-founder of a2z Radiology AI. S.D. is a part-time employee of a2z Radiology AI.

Figures

Fig. 1
Fig. 1. Current reimbursement pathways.
Flowchart of reimbursement pathways used by the Centers for Medicare and Medicaid (CMS). ICD International Classification of Diseases.
Fig. 2
Fig. 2. Reimbursement for generalist radiology AI.
Three key areas of questions regarding reimbursement for generalist radiology AI (GRAI) along with possible approaches to help drive development of new pathways.
See this image and copyright information in PMC

References

    1. McNamara, S. L., Yi, P. H. & Lotter, W. The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation. npj Digital Med.7, 1–5 (2024). - PMC - PubMed
    1. Rothenberg, S. A. et al. Prospective Evaluation of AI Triage of Pulmonary Emboli on CT Pulmonary Angiograms. Radiology309, e230702 (2023). - PubMed
    1. Rajpurkar, P. & Lungren, M. P. The Current and Future State of AI Interpretation of Medical Images. N. Engl. J. Med.388, 1981–1990 (2023). - PubMed
    1. Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature616, 259–265 (2023). - PubMed
    1. Zhou, H.-Y., Adithan, S., Acosta, J. N., Topol, E. J. & Rajpurkar, P. A Generalist Learner for Multifaceted Medical Image Interpretation. arXiv [cs.CV] (2024).

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