Artificial Intelligence (AI) refers to the ability of a machine to independently replicate intellectual processes typical of human cognition. The use of AI across fields, including medicine, continues to grow as this technology becomes more ubiquitous.

By 2025, the growth rate of AI applications in health care is expected to be 24.5%.¹ The application of AI in urology ranges from diagnostic and prognostic roles to applications in surgical education and treatments. AI provides more accuracy and can aid in clinical decision-making and, therefore, will likely be an integral part of the health care system moving forward.²

The application of AI capabilities within the context of medical imaging is known as radiomics. Images can be analyzed for features, including shape, texture, intensity; these data then can be analyzed and associated with a clinical outcome. Within urological oncology, this analysis is applied to aid in cancer diagnosis. A study comparing a radiomics model interpretation of prostate MRI and that of radiologists showed that the radiomics model was able to outperform radiologists in diagnosing prostate cancer using the Prostate Imaging Reporting & Data System.³ In practice, we also can use machine learning to automatically segment transrectal ultrasound images of the prostate to aid in such procedures as fusion biopsies with smaller margins of error than when manually segmented.⁴ Imaging also can be analyzed for prognosis. For example, in urolithiasis, AI technology can predict stone composition and potential stone passage, enabling urologists to make informed decisions regarding medical expulsive therapy vs surgical intervention.² Along the same lines, researchers can use AI analysis to predict patients who may have early biochemical recurrence after prostatectomy, allowing appropriate treatment plans to be formulated to improve outcomes and quality of life.⁵

For surgical education and training, AI can be used to provide in-depth analysis of robotic surgery and assist in these surgeries. Machine learning can analyze data from the da Vinci Surgical System, including instrument kinetic data, motion tracking, and systems event data (ie, camera movement or energy usage) in conjunction with the surgical video itself and report metrics associated with surgical efficiency.⁶ Comparing these metrics between expert and novice surgeons shows differences between the 2 groups and can provide further guidance and feedback to trainees during robotic cases. Trainees will be able to reflect on this information to better track their performance on specific steps, as well as the overall surgery. As more work in this field is completed, it may help to standardize robotic training across institutions.

In conclusion, AI is transforming the field of urology and enabling urologists to provide more accurate diagnoses, personalized treatment plans, and innovated surgical technique. This will ultimately lead to improved health care costs and patient outcomes by avoiding unnecessary tests and procedures and focusing on more targeted treatments. As the technology continues to advance and become widely available, it will change the way urologists practice and train across the country. As a junior trainee, it is an exciting opportunity to see this technology become implemented and how our current practice techniques continue to be modified and improved.

Since its inception in 2002, the Residents and Fellows Committee has represented the voice of trainee members of the AUA. The Committee’s mission is to address the educational and professional needs of urology residents and fellows and promote engagement between residents and fellows and the AUA. The Committee welcomes your input and feedback! To contact the Committee, or to inquire about ways to get more involved, please email rescommittee@AUAnet.org.