Artificial intelligence in urology

Raghav Gupta; Adriana Marcela Pedraza Bermeo; Krunal Pandav; Ashutosh Kumar Tewari

doi:10.1016/B978-0-443-15688-5.00051-6

Artificial intelligence in urology

Raghav Gupta
, Adriana Marcela Pedraza Bermeo
, Krunal Pandav
, Ashutosh Kumar Tewari

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

The chapter reviews the current state of AI application in the field of urology and the role AI can play in assisting clinicians across various urologic subspecialities including urologic oncology, endourology, and andrology. The application of AI within urology thus far is summarized, and current examples of AI implementation within the field are provided encompassing the diagnosis of urological disease, the prediction of outcomes following urologic intervention, and the assessment of surgical performance/skill. The challenges to the widespread use and adoption of AI applications in urology are highlighted, including the paucity of multiinstitutional studies, a lack of understanding of the “science behind AI” amongst clinicians, and a significant cost associated with AI-related technologies. Finally, the future directions of AI application in urology are suggested.

Original language	English
Title of host publication	Artificial Intelligence in Clinical Practice
Subtitle of host publication	How AI Technologies Impact Medical Research and Clinics
Publisher	Elsevier
Pages	259-265
Number of pages	7
ISBN (Electronic)	9780443156885
ISBN (Print)	9780443156892
DOIs	https://doi.org/10.1016/B978-0-443-15688-5.00051-6
State	Published - 1 Jan 2023

Keywords

Artificial intelligence
andrology
artificial neural networks (ANN)
endourology
machine learning
oncology
urology

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10.1016/B978-0-443-15688-5.00051-6

Cite this

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title = "Artificial intelligence in urology",

abstract = "The chapter reviews the current state of AI application in the field of urology and the role AI can play in assisting clinicians across various urologic subspecialities including urologic oncology, endourology, and andrology. The application of AI within urology thus far is summarized, and current examples of AI implementation within the field are provided encompassing the diagnosis of urological disease, the prediction of outcomes following urologic intervention, and the assessment of surgical performance/skill. The challenges to the widespread use and adoption of AI applications in urology are highlighted, including the paucity of multiinstitutional studies, a lack of understanding of the “science behind AI” amongst clinicians, and a significant cost associated with AI-related technologies. Finally, the future directions of AI application in urology are suggested.",

keywords = "Artificial intelligence, andrology, artificial neural networks (ANN), endourology, machine learning, oncology, urology",

author = "Raghav Gupta and \{Pedraza Bermeo\}, \{Adriana Marcela\} and Krunal Pandav and Tewari, \{Ashutosh Kumar\}",

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AU - Gupta, Raghav

AU - Pedraza Bermeo, Adriana Marcela

AU - Pandav, Krunal

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Y1 - 2023/1/1

N2 - The chapter reviews the current state of AI application in the field of urology and the role AI can play in assisting clinicians across various urologic subspecialities including urologic oncology, endourology, and andrology. The application of AI within urology thus far is summarized, and current examples of AI implementation within the field are provided encompassing the diagnosis of urological disease, the prediction of outcomes following urologic intervention, and the assessment of surgical performance/skill. The challenges to the widespread use and adoption of AI applications in urology are highlighted, including the paucity of multiinstitutional studies, a lack of understanding of the “science behind AI” amongst clinicians, and a significant cost associated with AI-related technologies. Finally, the future directions of AI application in urology are suggested.

AB - The chapter reviews the current state of AI application in the field of urology and the role AI can play in assisting clinicians across various urologic subspecialities including urologic oncology, endourology, and andrology. The application of AI within urology thus far is summarized, and current examples of AI implementation within the field are provided encompassing the diagnosis of urological disease, the prediction of outcomes following urologic intervention, and the assessment of surgical performance/skill. The challenges to the widespread use and adoption of AI applications in urology are highlighted, including the paucity of multiinstitutional studies, a lack of understanding of the “science behind AI” amongst clinicians, and a significant cost associated with AI-related technologies. Finally, the future directions of AI application in urology are suggested.

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KW - andrology

KW - artificial neural networks (ANN)

KW - endourology

KW - machine learning

KW - oncology

KW - urology

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SP - 259

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BT - Artificial Intelligence in Clinical Practice

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ER -

Artificial intelligence in urology

Abstract

Keywords

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