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Authors
Calleri, Alberto
Yazarkan, Yigit
Liu, Kan
Kassmeyer, Blake A.
Lennon, Ryan J.
Rattan, Puru
Seid, Amir
Bernard, Matthew E.
Singh, Gagandeep
Deyo-Svendsen, Mark E.
King, Graham
Stacey, Stephen K.
Olofson, Amy
Allen, Alina
Ahn, Joseph C.
Friedman, Paul A.
Kamath, Patrick S.
Attia, Zachi I.
Noseworthy, Peter A.
Shah, Vijay H.
Rushlow, David
Simonetto, Douglas A.
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Funding
Funding for this research was provided by:
Mayo Clinic (MAX Innovation Award, UL1TR002377)
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Article History
Received: 3 February 2026
Accepted: 27 April 2026
First Online: 9 May 2026
Competing interests
: The ECG-enabled machine-learning model for detection of advanced chronic liver disease was licensed by Mayo Clinic to Anumana. D.A.S., P.R., J.C.A., P.A.F., P.S.K., Z.I.A., P.A.N., and V.H.S. may benefit financially from its commercialization (patent US20230218238A1). Z.I.A. serves on the scientific advisory board of Anumana and acts as a consultant for Anumana, AliveCor, and XAI.health. P.A.F. serves on the scientific advisory board of Anumana. D.A.S. has consulted for Mallinckrodt, Evive, Resolution Therapeutics, BioVie, AstraZeneca, Iota and PharmaIN. The other authors declare no competing financial or non-financial interests.
