Machine learning in renal cell carcinoma research: the promise and pitfalls of 'renal-izing' the potential of artificial intelligence
BJU Int
.
2023 Aug;132(2):231-232.
doi: 10.1111/bju.16016.
Epub 2023 Apr 10.
Authors
Zine-Eddine Khene
1
2
,
Alexander Kutikov
3
,
Riccardo Campi
4
5
6
;
EAU-YAU Renal Cancer Working Group
Affiliations
1
Department of Urology, Rennes University Hospital, Rennes, France.
2
Signal and Image Processing Laboratory (LTSI), University of Rennes INSERM, Rennes, France.
3
Division of Urologic Oncology, Department of Surgical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA.
4
Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, Florence, Italy.
5
Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
6
European Association of Urology (EAU) Young Academic Urologists (YAU) Renal Cancer Working Group, Arnhem, The Netherlands.
PMID:
37005367
DOI:
10.1111/bju.16016
No abstract available
Publication types
Letter
Comment
MeSH terms
Algorithms
Artificial Intelligence
Carcinoma, Renal Cell* / diagnosis
Carcinoma, Renal Cell* / therapy
Humans
Kidney Neoplasms*
Machine Learning