Artificial Intelligence in Pediatric Urology.

Algorithm Artificial intelligence Machine learning Model Pediatric urology Prediction

Journal

The Urologic clinics of North America
ISSN: 1558-318X
Titre abrégé: Urol Clin North Am
Pays: United States
ID NLM: 0423221

Informations de publication

Date de publication:
Feb 2024
Historique:
medline: 13 11 2023
pubmed: 10 11 2023
entrez: 9 11 2023
Statut: ppublish

Résumé

Application of artificial intelligence (AI) is one of the hottest topics in medicine. Unlike traditional methods that rely heavily on statistical assumptions, machine learning algorithms can identify highly complex patterns from data, allowing robust predictions. There is an abundance of evidence of exponentially increasing pediatric urologic publications using AI methodology in recent years. While these studies show great promise for better understanding of disease and patient care, we should be realistic about the challenges arising from the nature of pediatric urologic conditions and practice, in order to continue to produce high-impact research.

Identifiants

pubmed: 37945105
pii: S0094-0143(23)00075-7
doi: 10.1016/j.ucl.2023.08.002
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

91-103

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Auteurs

Hsin-Hsiao Scott Wang (HH)

Computational Healthcare Analytics Program, Department of Urology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, USA. Electronic address: scottwang3@gmail.com.

Ranveer Vasdev (R)

Department of Urology, Mayo Clinic Rochester, 200 1st Street Southwest, Rochester, MN 55905, USA.

Caleb P Nelson (CP)

Clinical and Health Services Research, Department of Urology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, USA.

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Classifications MeSH