Decision support systems in the diagnosis of urological diseases

Abstract

The need to process large amounts of data has led to the creation of software that can improve and facilitate the work of medical staff. Decision support systems (DSS) are now used in many branches of medicine both at the outpatient and inpatient stages of medical care, helping clinicians to choose the tactics of treatment and management of each individual patient. These systems to a certain extent can improve treatment results and diagnostic process. The introduction of DSS in clinical practice has shown many advantages in reducing the frequency of misdiagnosis and, consequently, the risk of medical errors. At the same time, DSS can have a number of disadvantages. For example, physicians may view them as a threat to their “clinical autonomy”, and the implementation and subsequent maintenance of DSS can be quite costly. Artificial intelligence, which is increasingly being used not only for diagnosis, but also for treatment and prediction of outcomes in various diseases, should be considered as a prerequisite for the creation of DSS. Active development of artificial intelligence has been noted in almost all branches of medicine. A non-systematic review of the available literature published in the period between 2012 and 2022 has shown that the application of AI in prostate cancer diagnosis has great potential in clinical practice, as it helps both in the choice of treatment method and in planning the course of further surgery.

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