Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement
Matteo Ferro, Ugo Giovanni Falagario, Biagio Barone, Martina Maggi, Felice Crocetto, Gian Maria Busetto, Francesco Del Giudice, Daniela Terracciano, Giuseppe Lucarelli, Francesco Lasorsa, Michele Catellani, A. Brescia, Francesco Alessandro Mistretta, Stefano Luzzago, Mattia Luca Piccinelli, Mihai Dorin Vartolomei, Barbara Alicja Jereczek‐Fossa, Gennaro Musi, E. Montanari, Ottavio De Cobelli, Octavian Sabin Tătaru
Abstract
Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.