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.

Robot-assisted surgical systems (RASS) have revolutionised the management of many urological conditions over the last two decades with robot-assisted radical prostatectomy (RARP) now being considered by many to be the preferred surgical approach. Intuitive Surgical has dominated the market during this time period with successive iterations of the da Vinci model. The expiration of patents has opened the RASS market and several new contenders have become available or are currently in development. This comprehensive narrative review aims to explore the merits of each robotic system as well as the evidence and barriers to their use. The newly developed RASS have increased the versality of robotic surgical systems to a wider range of settings through advancement in technology. The increased competition may result in an overall reduction in cost, broadening the accessibility of RASS. Learning curves and training remain a barrier to their use, but the situation appears to be improving through dedicated training programmes. Outcomes for RARP have been well investigated and tend to support improved early functional outcomes. Overall, the rapid developments in the field of robot-assisted surgery indicate the beginning of a promising new era to further enhance urological surgery.

Artificial intelligence (AI) is an emerging technological system that provides a platform to manage and analyze data by emulating human cognitive functions with greater accuracy, revolutionizing patient care and introducing a paradigm shift to the healthcare industry. The purpose of this study is to identify the underlying factors that affect the adoption of artificial intelligence in healthcare (AIH) by healthcare providers and to understand “What are the factors that influence healthcare providers’ behavioral intentions to adopt AIH in their routine practice?” An integrated survey was conducted among healthcare providers, including consultants, residents/students, and nurses. The survey included items related to performance expectancy, effort expectancy, initial trust, personal innovativeness, task complexity, and technology characteristics. The collected data were analyzed using structural equation modeling. A total of 392 healthcare professionals participated in the survey, with 72.4% being male and 50.7% being 30 years old or younger. The results showed that performance expectancy, effort expectancy, and initial trust have a positive influence on the behavioral intentions of healthcare providers to use AIH. Personal innovativeness was found to have a positive influence on effort expectancy, while task complexity and technology characteristics have a positive influence on effort expectancy for AIH. The study’s empirically validated model sheds light on healthcare providers’ intention to adopt AIH, while the study’s findings can be used to develop strategies to encourage this adoption. However, further investigation is necessary to understand the individual factors affecting the adoption of AIH by healthcare providers.