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

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

doi:10.1016/j.ucl.2021.07.009

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

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

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.]

Original language	English
Pages (from-to)	65-117
Number of pages	53
Journal	Urologic Clinics of North America
Volume	49
Issue number	1
Early online date	23 Oct 2021
DOIs	https://doi.org/10.1016/j.ucl.2021.07.009
Publication status	Published - Feb 2022

Keywords

Artificial intelligence
Deep learning
Machine learning
Review
Urology

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10.1016/j.ucl.2021.07.009

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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 {\textcopyright} 2021 Elsevier Inc. All rights reserved.]",

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

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AU - Chen, Andrew B.

AU - Haque, Taseen

AU - Roberts, Sidney

AU - Rambhatla, Sirisha

AU - Cacciamani, Giovanni

AU - Dasgupta, Prokar

AU - Hung, Andrew J.

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

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