Introduction

As the frontier of health care rapidly evolves, we are thrilled to present an exclusive preview of an upcoming panel discussion at the AUA2024 conference that promises to delve deep into the transformative potential of artificial intelligence (AI) in urology. From enhancing diagnostic precision in prostate cancer through radiomics to revolutionizing bladder cancer treatment with pathomics and improving surgical planning and execution, this panel discussion is set to illuminate the myriad ways in which AI is reshaping health care (Figure).

What Is AI/Machine Learning/Deep Learning Application in Surgery?

Dr Andrew J. Hung from Cedars-Sinai Medical Center will give an overview regarding “surgical AI,” an emerging field at the intersect of surgery and AI, and explain the most commonly encountered terms.¹ AI stands as the general term under which machine learning (ML) and deep learning (DL) find their places.² AI’s application in surgery manifests through algorithms and computational models designed to simulate human intelligence, offering advancements in diagnostic accuracy, patient-specific treatment plans, and operative precision. ML serves as a crucial subfield within AI, providing the means for computers to gain insights and make sense of data. AI aims to replicate human cognitive functions, and ML offers the methodologies necessary for this imitation. Through the analysis of data, ML algorithms enable AI systems to evolve and enhance their capabilities over time. DL, a specialized branch of ML, leverages intricate neural networks to handle sophisticated tasks. DL is characterized by its use of artificial neural networks modeled after the human brain’s architecture. These networks comprise multiple layers of nodes or neurons, which process incoming data and relay them through the network. This structure allows for the learning of data’s hierarchical features. DL is particularly adept at analyzing and identifying patterns within vast datasets, including images, sounds, and texts, by learning from unstructured data. The synergy of AI, ML, and DL in surgery not only propels the field towards unprecedented technological heights but also promises significant improvements in patient outcomes and health care efficiencies.

AI and Radiomics in Prostate Cancer

Dr Geoffrey A. Sonn from Stanford University will illuminate the profound impact of AI and radiomics on prostate cancer diagnostics and management. Investigations into AI’s role in diagnosing prostate cancer are progressing quickly, offering the potential to improve every facet of the existing diagnostic approach, advancing the precision in detecting, characterizing, and stratifying prostate cancer risk, underscoring the crucial role of technology in tailoring patient-specific therapeutic strategies. While a vast amount of scholarly work discusses AI applications in prostate cancer detection, the majority of these innovations have not advanced to a stage where they can be implemented in clinical settings.³ Dr Sonn will address the challenges of integrating these technological advancements into current clinical frameworks, emphasizing the necessity for ongoing research and multidisciplinary collaboration to bridge the gap between theoretical innovation and practical clinical application.

AI and Pathomics in Bladder Cancer

Dr Joseph C. Liao from Stanford University will use bladder cancer (BC) as an example to showcase how AI is used in pathomics. The examination of tumor tissue through pathology remains the gold standard in diagnosing and determining the risk level of bladder cancer. AI-enhanced pathology tools are emerging as significant aids in improving diagnostic precision and assisting in the risk assessment for BC patients, playing a crucial role in shaping treatment strategies and future outlooks. Several research teams have crafted DL algorithms capable of forecasting BC progression by analyzing clinical and pathological data.⁴ These AI-driven models are pivotal for pinpointing patients at elevated risk, necessitating more intensive treatments or adjusted monitoring plans. This synergy of AI with traditional cytology and pathology is opening new paths for advancing BC treatment and enhancing patient care outcomes.

AI in Surgical Planning and Execution

Dr Prokar Dasgupata from King’s College London will shed light on how AI can be used in surgical planning and execution. Computer vision, a science of using AI to analyze images and videos, is revolutionizing how surgeons perform and teach surgery. AI has been used in surgical phase recognition, in other words, recognizing different surgical steps and substeps, which can provide valuable information for surgical education and facilitate real-time surgical workflow monitoring for operating room management.^5,6 More granularly, AI can follow the motion of surgical instruments and recognize which exact surgical gesture is being used.^1,7 Those are the building blocks for more complex tasks such as intraoperative intelligent assistance or automatic surgery.

As we approach the AUA2024 conference, the anticipation for the “Artificial Intelligence Plenary Discussion” underscores the medical community’s commitment to embracing the future. The insights from Drs Hung, Sonn, Liao, and Dasgupta exemplify the pioneering spirit of the urological field, showcasing AI’s capacity to revolutionize not just urology but health care at large. As we delve into the complexities and potentials of AI, ML, and DL, let us move forward with the knowledge that the future of urology, powered by AI, is not just approaching—it’s here.