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Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework
Jethro C C Kwong1, Louise C McLoughlin1, Masoom Haider2
1Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada.
Abstract:
The Standardized Reporting of Machine Learning Applications in Urology (STREAM-URO) framework was developed to provide a set of recommendations to help standardize how machine learning studies in urology are reported. This framework serves three purposes: (1) to promote high-quality studies and streamline the peer review process; (2) to enhance reproducibility, comparability, and interpretability of results; and (3) to improve engagement and literacy of machine learning within the urological community.
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