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Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

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 im...

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Detalles Bibliográficos
Autores principales: Ferro, Matteo, Falagario, Ugo Giovanni, Barone, Biagio, Maggi, Martina, Crocetto, Felice, Busetto, Gian Maria, del Giudice, Francesco, Terracciano, Daniela, Lucarelli, Giuseppe, Lasorsa, Francesco, Catellani, Michele, Brescia, Antonio, Mistretta, Francesco Alessandro, Luzzago, Stefano, Piccinelli, Mattia Luca, Vartolomei, Mihai Dorin, Jereczek-Fossa, Barbara Alicja, Musi, Gennaro, Montanari, Emanuele, de Cobelli, Ottavio, Tataru, Octavian Sabin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340656/
https://www.ncbi.nlm.nih.gov/pubmed/37443700
http://dx.doi.org/10.3390/diagnostics13132308
Descripción
Sumario: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.