Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. The article provides an insight into the status and prospects of big data analytics in healthcare, highlights the advantages, describes the frameworks and techniques used, briefs about the challenges faced currently, and discusses viable solutions. Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.

Objective: Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed. Material and methods: Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords “urology,” “artificial intelligence,” “machine learning,” “deep learning,” “artificial neural networks,” “computer vision,” and “natural language processing” were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. Results: The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively. Conclusions: In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process. Cite this article as: Shah M, Naik N, Somani BK, Hameed BMZ. Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study. Turk J Urol 2020; 46(Supp. 1): S27-S39.

The COVID-19 pandemic has put a strain on the entire global healthcare infrastructure. The pandemic has necessitated the re-invention, re-organization, and transformation of the healthcare system. The resurgence of new COVID-19 virus variants in several countries and the infection of a larger group of communities necessitate a rapid strategic shift. Governments, non-profit, and other healthcare organizations have all proposed various digital solutions. It's not clear whether these digital solutions are adaptable, functional, effective, or reliable. With the disease becoming more and more prevalent, many countries are looking for assistance and implementation of digital technologies to combat COVID-19. Digital health technologies for COVID-19 pandemic management, surveillance, contact tracing, diagnosis, treatment, and prevention will be discussed in this paper to ensure that healthcare is delivered effectively. Artificial Intelligence (AI), big data, telemedicine, robotic solutions, Internet of Things (IoT), digital platforms for communication (DC), computer vision, computer audition (CA), digital data management solutions (blockchain), digital imaging are premiering to assist healthcare workers (HCW's) with solutions that include case base surveillance, information dissemination, disinfection, and remote consultations, along with many other such interventions.

Recent advances in artificial intelligence (AI) have certainly had a significant impact on the healthcare industry. In urology, AI has been widely adopted to deal with numerous disorders, irrespective of their severity, extending from conditions such as benign prostate hyperplasia to critical illnesses such as urothelial and prostate cancer. In this article, we aim to discuss how algorithms and techniques of artificial intelligence are equipped in the field of urology to detect, treat, and estimate the outcomes of urological diseases. Furthermore, we explain the advantages that come from using AI over any existing traditional methods.

Ureteral access sheaths (UASs) are part of urologist’s armamentarium when performing retrograde intrarenal surgery (RIRS). Recently, the world of RIRS has changed dramatically with the development of three game-changers: thulium fiber laser (TFL), smaller size single use digital flexible ureterosopes and intraoperative intrarenal pressure (IRP) measurement devices. We aimed to clarify the impact of UASs on IRP, complications and SFRs and put its indications in perspective of these three major technological improvements. A systematic review of the literature using the Medline, Scopus and Web of Science databases was performed by two authors and relevant studies were selected according to PRISMA guidelines. Recent studies showed that using a UAS lowers IRP and intrarenal temperature by increasing irrigation outflow during RIRS. Data on the impact of a UAS on SFRs, postoperative pain, risk of infectious complications, risk of ureteral strictures and risk of bladder recurrence of urothelial carcinoma after diagnostic RIRS were inconclusive. Prestenting for at least one week resulted in ureteral enlargement, while the influence of pre-operative administration of alpha-blockers was unclear. Since TFL, smaller single use digital ureteroscopes and devices with integrated pressure-measuring and aspiration technology seemed to increase SFRs and decrease pressure and temperature related complications, indications on the use of a UAS may decrease in the near future.

Purpose of Review: Kidney stone disease (KSD) and recurrent urinary tract infections (rUTI) are frequently concomitant conditions. We conducted a systematic review to determine the association of UTI in patients with KSD and to assess the outcomes of kidney stone treatment in the resolution of rUTI. Recent Findings: Our systematic review included 17 papers and a strong association between KSD and rUTI was demonstrated by numerous studies. Surgical clearance of kidney stones usually resulted in the resolution of UTI, but discordant data persist regarding recurrence rates after surgery. In vitro studies might unveil the causative role of bacteria in the formation of “metabolic” stones. Summary: Our SR clearly shows that UTI and KSD are mutually coexisting, and reciprocally causal and such patients should be counselled for proactive intervention by stone removal especially when UTIs are recurrent or additional risk factors are present irrespective of stone composition. To prevent further UTI episodes, if possible, a stone culture must be obtained for an effectively targeted antibiotic treatment regime tailored to bacterial prevalence.