Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists.

Journal: The Urologic clinics of North America
PMID: 34776055

Abstract

The growth and adoption of artificial intelligence has led to impressive results in urology. As artificial intelligence grows more ubiquitous, it is important to establish artificial intelligence literacy in the workforce. To this end, we present a narrative review of the literature of artificial intelligence and machine learning in urology and propose a checklist of reporting standards to improve readability and evaluate the current state of the literature. The listed article demonstrated heterogeneous reporting of methodologies and outcomes, limiting generalizability of research. We hope that this review serves as a foundation for future evaluation of medical research in artificial intelligence.

Authors

  • Andrew B Chen
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, CA.
  • Taseen Haque
    Keck School of Medicine of USC, 1441 Eastlake Avenue Suite 7416, Los Angeles, CA 90089, USA.
  • Sidney Roberts
    Keck School of Medicine of USC, 1441 Eastlake Avenue Suite 7416, Los Angeles, CA 90089, USA.
  • Sirisha Rambhatla
    Computer Science Department, University of Southern California, Los Angeles, CA, U.S.A.
  • Giovanni Cacciamani
    Department of Urology, Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
  • Prokar Dasgupta
  • Andrew J Hung
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California. Electronic address: Andrew.Hung@med.usc.edu.

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