Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology
0
Zitationen
18
Autoren
2026
Jahr
Abstract
Race/ethnicity reporting is poor in most AI studies in pediatric urology. Standardized reporting may help ensure fairness and generalizability of models across diverse pediatric urology populations.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.485 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.371 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.827 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.549 Zit.
Autoren
Institutionen
- University of British Columbia(CA)
- University of Toronto(CA)
- Public Health Ontario(CA)
- Schwartz/Reisman Emergency Medicine Institute(CA)
- Hospital for Sick Children(CA)
- Cincinnati Children's Hospital Medical Center(US)
- Johns Hopkins University(US)
- Johns Hopkins Medicine(US)
- Riley Hospital for Children(US)
- Children's Hospital of Los Angeles(US)
- Seattle Children's Hospital(US)
- Children's Hospital of Philadelphia(US)
- Erasmus MC - Sophia Children’s Hospital(NL)
- Izaak Walton Killam Health Centre(CA)
- Boston Children's Hospital(US)
- Cleveland Clinic(US)