As we enter the era of "big data", an increasing amount of complex health- care data will become available. These data are often redundant, "noisy", and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the Artificial Intelligence (AI) with Machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in Urology.

Artificial intelligence and neural networks in urology: current clinical applications / Checcucci, Enrico; Autorino, Riccardo; Cacciamani, Giovanni E; Amparore, Daniele; De Cillis, Sabrina; Piana, Alberto; Piazzolla, Pietro; Vezzetti, Enrico; Fiori, Cristian; Veneziano, Domenico; Tewari, Ash; Dasgupta, Prokar; Hung, Andrew; Gill, Inderbir; Porpiglia, Francesco. - In: MINERVA UROLOGICA E NEFROLOGICA. - ISSN 1827-1758. - (2020). [10.23736/S0393-2249.19.03613-0]

Artificial intelligence and neural networks in urology: current clinical applications

Piazzolla, Pietro;Vezzetti, Enrico;
2020

Abstract

As we enter the era of "big data", an increasing amount of complex health- care data will become available. These data are often redundant, "noisy", and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the Artificial Intelligence (AI) with Machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in Urology.
File in questo prodotto:
File Dimensione Formato  
Paper Minerva.pdf

accesso riservato

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 787.44 kB
Formato Adobe PDF
787.44 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2776876