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.
Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement / Ferro, Matteo; Giovanni Falagario, Ugo; Barone, Biagio; Maggi, Martina; Crocetto, Felice; Busetto, GIAN MARIA; DEL GIUDICE, Francesco; Terracciano, Daniela; Lucarelli, Giuseppe; Lasorsa, Francesco; Catellani, Michele; Brescia, Antonio; Alessandro Mistretta, Francesco; Luzzago, Stefano; Luca Piccinelli, Mattia; Dorin Vartolomei, Mihai; Alicja Jereczek-Fossa, Barbara; Musi, Gennaro; Montanari, Emanuele; de Cobelli, Ottavio; Sabin Tataru, Octavian. - In: DIAGNOSTICS. - ISSN 2075-4418. - (2023). [10.3390/diagnostics13132308]
Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement
Matteo Ferro;Martina Maggi;Gian Maria Busetto;Francesco del Giudice;Giuseppe Lucarelli;Antonio Brescia;
2023
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.| File | Dimensione | Formato | |
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