Andrew B. Chen, Taseen Haque, Sidney Roberts, Sirisha Rambhatla, Giovanni Cacciamani, Prokar Dasgupta, Andrew J. Hung
| Original language | English |
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| Pages (from-to) | 65-117 |
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| Number of pages | 53 |
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| Journal | Urologic Clinics of North America |
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| Volume | 49 |
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| Issue number | 1 |
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| Early online date | 23 Oct 2021 |
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| DOIs | |
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| Accepted/In press | 2021 |
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| E-pub ahead of print | 23 Oct 2021 |
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| Published | Feb 2022 |
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| Additional links | |
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Funding Information:
Research reported in this publication was supported in part by the National Cancer Institute under Award No. R01CA251579-01A1.
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.]