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PURPOSE: Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. MATERIALS AND METHODS: A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. RESULTS: The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. CONCLUSIONS: Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Original publication

DOI

10.1016/j.juro.2007.05.122

Type

Journal

J Urol

Publication Date

10/2007

Volume

178

Pages

1150 - 1156

Keywords

Artificial Intelligence, Decision Making, Computer-Assisted, Disease Progression, Expert Systems, Humans, Kidney Neoplasms, Male, Neural Networks (Computer), Prognosis, Prostatic Neoplasms, Sensitivity and Specificity, Survival Rate, Testicular Neoplasms, Urinary Bladder Neoplasms, Urogenital Neoplasms