Virtual "Matchmaking": Stakeholder Perspectives on the Future of the Urology Residency Match.

OBJECTIVE: To examine stakeholder perspectives regarding the lack of in-person externships and transition to a virtual urology residency interview format. The unprecedented disruption from the COVID-19 pandemic forced an abrupt pivot to a "virtual" Urology Match for the 2021 cycle. We aim for our study to inform ongoing deliberations on the future of the Urology Match. MATERIALS AND METHODS: Following Urology Match day in February 2021, two surveys were distributed by the Society of Academic Urologists to all applicants and program directors (PDs) who participated in the 2021 Urology Match. RESULTS: Overall, 192 of 481 applicants (40%) and 63 of 160 PDs (39%) responded. Most applicants (67%) were satisfied with their match outcomes, although unmatched applicants were significantly more likely to be unsatisfied than matched applicants (98% vs 9%, P <.0001). Most PDs were equally (79%) or more satisfied (13%) with their match outcomes compared with prior years. Nearly all applicants (93%) and PDs (94%) recommended retaining an in-person externship option. Most applicants (61%) and PDs (71%) felt their outcomes would not have changed with in-person interviews. Applicants and PDs were evenly split as to whether interviews should be conducted in-person or virtually in the future. CONCLUSION: The vast majority of applicants and PDs recommended retaining in-person externships for future match cycles despite high costs. In contrast, there was ambivalence amongst both groups of stakeholders regarding the format of interviews for future match cycles. We recommend virtual interviews moving forward to help alleviate the financial burden placed on applicants and increase equity.

Clinical applications of artificial intelligence in urologic oncology.

PURPOSE OF REVIEW: This review aims to shed light on recent applications of artificial intelligence in urologic oncology. RECENT FINDINGS: Artificial intelligence algorithms harness the wealth of patient data to assist in diagnosing, staging, treating, and monitoring genitourinary malignancies. Successful applications of artificial intelligence in urologic oncology include interpreting diagnostic imaging, pathology, and genomic annotations. Many of these algorithms, however, lack external validity and can only provide predictions based on one type of dataset. SUMMARY: Future applications of artificial intelligence will need to incorporate several forms of data in order to truly make headway in urologic oncology. Researchers must actively ensure future artificial intelligence developments encompass the entire prospective patient population.