Abstract
Reported instances of AI-assisted, blanket denials of coverage have increased in recent years, particularly for Medicare Advantage plans, resulting in insurers facing criticism, class action lawsuits, investigations from Congress, and key providers leaving their networks. To ensure a fair healthcare system, action is needed to improve transparency in how AI tools approve or deny claims, and address provider burnout and patient burden due to navigating prior authorization requests and appeals.
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S.R. drafted the initial manuscript. S.R., S.G., C.S., R.H.-S., and C.Sh. revised and edited the manuscript. All authors read and approved the final version.
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S.R. reports receiving grants from the Benton Institute for Broadband & Society. These interests are unrelated to the focus on this Perspective. Outside the submitted work, S.G. reports receiving grants from the European Union (Grant Agreement no. 101057321 and no. 101057099), the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the National Institutes of Health Office of the Director (NIH OD) (Grant Agreement no. 1R21EB035474-01), and the Cancer Center at Illinois. S.G. is also a Research Fellow at the University of Copenhagen, Faculty of Law, supported by a Novo Nordisk Foundation Grant for a scientifically independent International Collaborative Bioscience Innovation & Law Program (Inter-CeBIL program - grant no. NNF23SA0087056). C.S. declares no competing interests. R.H.-S. declares no competing interests. C.Sh. reports receiving grants from Gilead, Bristol Myers Squibb Foundation, Viiv, Commonwealth Foundation, and Kaiser Permanente. These interests are unrelated to the focus of this Perspective. She currently serves on the board of the Fishing Partnership Support Services as well as on the Ethics Committee of the American College of Obstetricians and Gynecologists (ACOG).
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Raza, S., Gerke, S., Silcox, C. et al. Medicare advantage becoming a disadvantage with use of artificial intelligence in prior authorization review. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02387-x
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DOI: https://doi.org/10.1038/s41746-026-02387-x
