AI-driven scientometric analysis with recency insights: lower urinary tract symptoms in older and frail populations.

Journal: World journal of urology
Published Date: Jul 16, 2025

Abstract

PURPOSE: This artificial intelligence (AI)-driven scientometric analysis, conducted using the Mynd discovery platform, explores research trends in lower urinary tract symptoms (LUTS) among older patients. By applying its novel recency metric, the study identified emerging areas, longstanding research themes, and critical gaps in literature.

Authors

  • Andries Van Huele
    Department of Urology, AZ Alma, Eeklo, Belgium. Andries.Vanhuele@ugent.be.
  • Jelle Demeulemeester
    Mynd-Ware, Vaartstraat 130, Kortrijk, Belgium.
  • Karel Everaert
    Department of Urology, Ghent University Hospital, Ghent, Belgium.
  • Mirko Petrovic
    Department of Internal Medicine (Geriatrics), Ghent University, Ghent, Belgium.
  • Patrick Calders
    Department of Rehabilitation Sciences, Occupational Therapy, Physiotherapy and Speech-language Pathology/Audiology, Ghent University, Ghent, Belgium.
  • François Hervé
    Department of Urology, Ghent University Hospital, Ghent, Belgium.
  • Adrian Wagg
    Division of Geriatric Medicine, University of Alberta, Edmonton, AB, Canada.
  • George Bou Kheir
    Department of Urology, Ghent University Hospital, Ghent, Belgium.

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