Surgical Artificial Intelligence: Endourology

被引:2
|
作者
Tano, Zachary E. [1 ]
Cumpanas, Andrei D. [1 ]
Gorgen, Antonio R. H. [1 ]
Rojhani, Allen [1 ]
Altamirano-Villarroel, Jaime [1 ]
Landman, Jaime [1 ]
机构
[1] Univ Calif Irvine, Dept Urol, 3800 West Chapman Ave,Suite 7200, Orange, CA 92868 USA
关键词
Artificial intelligence; Machine learning; Endourology; Kidney stone; PCNL; Ureteroscopy; Benign prostatic hyperplasia; STONE-FREE STATUS; CLINICAL-RESEARCH OFFICE; MACHINE LEARNING-MODELS; SHOCK-WAVE LITHOTRIPSY; PERCUTANEOUS NEPHROLITHOTOMY; NEURAL-NETWORK; URETERAL STONES; KIDNEY-STONES; COMPUTED-TOMOGRAPHY; NATIONAL-HEALTH;
D O I
10.1016/j.ucl.2023.06.004
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Based on the number of studies explored here alone, one can see that AI is a very active subject of research in endourology. The potential benefit should impart a sense of excitement in the urologic field; however, AI should be met with cautious optimism. The mechanisms of ML and DL are complex and in the case of DL, unsupervised with regard to input and evaluation techniques. Similar to complex traditional statistical methods, urologists can rely on computer scientist interpretation of AI, as they rely on statisticians, to an extent. Urologists must familiarize themselves with AI for continued protection of patients by serving as the bridge between technologic innovation and clinical practice. Most of the AI studies are retrospective and theoretic; the ability for critical appraisals by urologists is the keystone to validating theoretic models and designing prospective studies for the benefits of AI to be realized in clinical practice.
引用
收藏
页码:77 / 89
页数:13
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