Abstract and Introduction
Purpose of Review: The aim of the present manuscript is to provide an overview on the current state of artificial intelligence (AI) tools in either decision making, diagnosis, treatment options, or outcome prediction in functional urology.
Recent Findings: Several recent studies have shed light on the promising potential of AI in functional urology to investigate lower urinary tract dysfunction pathophysiology but also as a diagnostic tool by enhancing the existing evaluations such as dynamic magnetic resonance imaging or urodynamics. AI may also improve surgical education and training because of its automated performance metrics recording. By bringing prediction models, AI may also have strong therapeutic implications in the field of functional urology in the near future. AI may also be implemented in innovative devices such as e-bladder diary and electromechanical artificial urinary sphincter and could facilitate the development of remote medicine.
Summary: Over the past decade, the enthusiasm for AI has been rising exponentially. Machine learning was well known, but the increasing power of processors and the amount of data available has provided the platform for deep learning tools to expand. Although the literature on the applications of AI technology in the field of functional urology is relatively sparse, its possible uses are countless especially in surgical training, imaging, urodynamics, and innovative devices.
Artificial Intelligence (AI), a concept which dates back to the 1950s, can be defined as a set of mathematical algorithms and computer programs that learn to perform tasks requiring types of intelligence usually found in human beings. Developments in automation and AI in the last decade have revolutionized the commercial industry and have demonstrated a great potential in healthcare.
Machine learning (ML), a subset of applied AI, is a generic expression for algorithms that aim to improve performance on specific tasks (Figure 1). It automates model building to extract patterns by learning from data. In medicine and healthcare, AI has been applied first to medical imaging analysis, in which AI systems have shown robust diagnostic performance in detecting various medical conditions, including skin lesion detection, and lymph node metastases secondary to breast cancer from tissue sections.
A variety of ML and AI techniques have been successfully studied in the field of urology in the last decade for diagnosis, classification, and prediction purposes.[5,6] These studies have demonstrated the potential for AI to be used by clinicians in daily practice and to improve patients' care. Although it is still in its infancy, the potential of AI use in functional urology is promising.
The aim of this review is to summarize the recent literature on AI use in functional urology and to explore the limitations and perspectives of such developments.
Curr Opin Urol. 2021;31(4):385-390. © 2021 Wolters Kluwer Health, Inc.