The BJUI Editorial Team's view on artificial intelligence and machine learning

George N Thalmann¹, Tobias Klatte², Nathan Papa³, Sigrid V Carlsson^{4

5

6}

Affiliations

¹ Department of Urology, University of Bern, Inselspital, Bern, Switzerland.
² Department of Urology, Charité - Universitaetsmedizin Berlin, Berlin, Germany.
³ School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
⁴ Department of Surgery (Urology Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁵ Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁶ Department of Urology, Sahlgrenska Academy at Gothenburg University, Gothenburg, Sweden.

Editorial

George N Thalmann et al. BJU Int. 2023 Aug.

. 2023 Aug;132(2):116-118.

doi: 10.1111/bju.16024. Epub 2023 Apr 27.

George N Thalmann¹, Tobias Klatte², Nathan Papa³, Sigrid V Carlsson^{4

5

6}

¹ Department of Urology, University of Bern, Inselspital, Bern, Switzerland.
² Department of Urology, Charité - Universitaetsmedizin Berlin, Berlin, Germany.
³ School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
⁴ Department of Surgery (Urology Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁵ Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁶ Department of Urology, Sahlgrenska Academy at Gothenburg University, Gothenburg, Sweden.

No abstract available

References

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1. Khene Z-E, Kutikov A, Campi R. The promise and pitfalls of renal-izing the potential of artificial intelligence. BJU Int 2023. https://doi.org/10.1111/bju.16016
1. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med 2019; 25: 30-6
1. Wong A, Otles E, Donnelly JP et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med 2021; 181: 1065-70
1. Roberts M, Driggs D, Thorpe M et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 2021; 3: 199-217