Projected Growth in FDA-Approved Artificial Intelligence Products Given Venture Capital Funding.

Autor:	McNabb NK; Data Science Analyst, ACR Data Science Institute, Reston, Virginia. Electronic address: nkm.mcnabb@gmail.com., Christensen EW; Director, Economic and Health Services Research, Harvey L. Neiman Health Policy Institute, Reston, Virginia, and Adjunct Professor, Health Services Management, University of Minnesota, St Paul, Minnesota., Rula EY; Executive Director, Harvey L. Neiman Health Policy Institute, Reston, Virginia., Coombs L; Vice President of Data Science and Informatics, ACR Data Science Institute, Reston, Virginia., Dreyer K; Chief Science Officer ACR Data Science Institute, Massachusetts General Hospital, Boston, Massachusetts., Wald C; Chair of American College of Radiology Informatics Commission, Lahey Hospital and Medical Center, Boston, Massachusetts., Treml C; Director of Data Science, ACR Data Science Institute, Reston, Virginia.
Jazyk:	angličtina
Zdroj:	Journal of the American College of Radiology : JACR [J Am Coll Radiol] 2024 Apr; Vol. 21 (4), pp. 617-623. Date of Electronic Publication: 2023 Oct 16.
DOI:	10.1016/j.jacr.2023.08.030
Abstrakt:	Purpose: Medical imaging accounts for 85% of digital health's venture capital funding. As funding grows, it is expected that artificial intelligence (AI) products will increase commensurately. The study's objective is to project the number of new AI products given the statistical association between historical funding and FDA-approved AI products. Methods: The study used data from the ACR Data Science Institute and for the number of FDA-approved AI products (2008-2022) and data from Rock Health for AI funding (2013-2022). Employing a 6-year lag between funding and product approved, we used linear regression to estimate the association between new products approved in a certain year, based on the lagged funding (ie, product-year funding). Using this statistical relationship, we forecasted the number of new FDA-approved products. Results: The results show that there are 11.33 (95% confidence interval: 7.03-15.64) new AI products for every $1 billion in funding assuming a 6-year lag between funding and product approval. In 2022 there were 69 new FDA-approved products associated with $4.8 billion in funding. In 2035, product-year funding is projected to reach $30.8 billion, resulting in 350 new products that year. Conclusions: FDA-approved AI products are expected to grow from 69 in 2022 to 350 in 2035 given the expected funding growth in the coming years. AI is likely to change the practice of diagnostic radiology as new products are developed and integrated into practice. As more AI products are integrated, it may incentivize increased investment for future AI products. (Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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