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Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%-17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation. (C) 2022 Editorial Office of Asian Journal of Urology. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Study Objective: Machine learning models have advanced medical image processing and can yield faster, more accurate diagnoses. Despite a wealth of available medical imaging data, high-quality labeled data for model training is lacking. We investigated whether a gamified crowdsourcing platform enhanced with inbuilt quality control metrics can produce lung ultrasound clip labels comparable to those from clinical experts. Methods: 2,384 lung ultrasound clips were retrospectively collected from 203 patients. Six lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create two sets of reference standard labels (195 training set clips and 198 test set clips). Sets were respectively used to A) train users on a gamified crowdsourcing platform, and B) compare concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Results: 99,238 crowdsourced opinions on 2,384 lung ultrasound clips were collected from 426 unique users over 8 days. On the 198 test set clips, mean labeling concordance of individual experts relative to the reference standard was 85.0% +/- 2.0 (SEM), compared to 87.9% crowdsourced label concordance (p=0.15). When individual experts' opinions were compared to reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs. 80.8% +/- 1.6; p, 27 pages total