Surgical Artificial Intelligence: Endourology

Zachary E Tano; Andrei D Cumpanas; Antonio R H Gorgen; Allen Rojhani; Jaime Altamirano-Villarroel; Jaime Landman

doi:10.1016/j.ucl.2023.06.004

Surgical Artificial Intelligence: Endourology

Urol Clin North Am. 2024 Feb;51(1):77-89. doi: 10.1016/j.ucl.2023.06.004. Epub 2023 Jul 31.

Authors

Zachary E Tano¹, Andrei D Cumpanas², Antonio R H Gorgen², Allen Rojhani², Jaime Altamirano-Villarroel², Jaime Landman²

Affiliations

¹ Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA. Electronic address: ztano@hs.uci.edu.
² Department of Urology, University of California, Irvine, 3800 West Chapman Avenue, Suite 7200, Orange, CA 92868, USA.

PMID: 37945104
DOI: 10.1016/j.ucl.2023.06.004

Abstract

Endourology is ripe with information that includes patient factors, laboratory tests, outcomes, and visual data, which is becoming increasingly complex to assess. Artificial intelligence (AI) has the potential to explore and define these relationships; however, humans might not be involved in the input, analysis, or even determining the methods of analysis. Herein, the authors present the current state of AI in endourology and highlight the need for urologists to share their proposed AI solutions for reproducibility outside of their institutions and prepare themselves to properly critique this new technology.

Keywords: Artificial intelligence; Benign prostatic hyperplasia; Endourology; Kidney stone; Machine learning; PCNL; Ureteroscopy.

Publication types

Review

MeSH terms

Artificial Intelligence*
Humans
Reproducibility of Results
Urologists*