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Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists

Research output: Contribution to journalArticlepeer-review

Andrew B. Chen, Taseen Haque, Sidney Roberts, Sirisha Rambhatla, Giovanni Cacciamani, Prokar Dasgupta, Andrew J. Hung

Original languageEnglish
Pages (from-to)65-117
Number of pages53
JournalUrologic Clinics of North America
Volume49
Issue number1
Early online date23 Oct 2021
DOIs
Accepted/In press2021
E-pub ahead of print23 Oct 2021
PublishedFeb 2022

Bibliographical note

Funding Information: Research reported in this publication was supported in part by the National Cancer Institute under Award No. R01CA251579-01A1.

King's Authors

Abstract

The growth and adoption of artificial intelligence has led to impressive results in urology. As artificial intelligence grows more ubiquitous, it is important to establish artificial intelligence literacy in the workforce. To this end, we present a narrative review of the literature of artificial intelligence and machine learning in urology and propose a checklist of reporting standards to improve readability and evaluate the current state of the literature. The listed article demonstrated heterogeneous reporting of methodologies and outcomes, limiting generalizability of research. We hope that this review serves as a foundation for future evaluation of medical research in artificial intelligence. [Abstract copyright: Copyright © 2021 Elsevier Inc. All rights reserved.]

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