{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T15:58:50Z","timestamp":1791907130772,"version":"4.3.4"},"reference-count":70,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T00:00:00Z","timestamp":1778976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>The convergence of artificial intelligence and robotic surgery is redefining the management of genitourinary cancers by enhancing diagnostic accuracy, surgical precision, and training efficiency. This narrative review explores recent advancements in artificial intelligence applications across the cancer care continuum, with a focus on prostate, kidney, and bladder malignancies. Artificial intelligence tools, particularly those based on machine learning and deep learning, have demonstrated strong performance in analyzing imaging data, segmenting tumors, predicting pathological features, and supporting clinical decision-making. Intraoperatively, artificial intelligence enables skill assessment, personalized feedback, and real-time navigation by processing data from surgical videos and robotic system sensors. Augmented reality and intraoperative modeling further enhance visualization and margin control during complex procedures. The review also discusses emerging technologies such as single-port robotic platforms, which offer advantages in confined anatomical spaces and support less invasive approaches. Additionally, the growing field of telesurgery is addressed, highlighting its feasibility for complex urologic operations across vast distances. While many of these innovations are still in early stages of clinical validation, their integration into practice has the potential to improve oncologic and functional outcomes, expand access to expert care, and foster the development of next-generation surgical strategies in urologic oncology.<\/jats:p>","DOI":"10.3390\/jcm15103856","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T12:36:40Z","timestamp":1779107800000},"page":"3856","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7028-1544","authenticated-orcid":false,"given":"Stamatios","family":"Katsimperis","sequence":"first","affiliation":[{"name":"Second Department of Urology, National and Kapodistrian University of Athens, Sismanogleio Hospital, 15126 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1972-0206","authenticated-orcid":false,"given":"Nikolaos","family":"Kostakopoulos","sequence":"additional","affiliation":[{"name":"First Department of Urology, Metropolitan General Hospital, 15562 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0390-1467","authenticated-orcid":false,"given":"Themistoklis","family":"Bellos","sequence":"additional","affiliation":[{"name":"Third Department of Urology, Mitera Hospital, 15123 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2547-8329","authenticated-orcid":false,"given":"Theodoros","family":"Spinos","sequence":"additional","affiliation":[{"name":"Department of Urology, University of Patras Hospital, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0427-4506","authenticated-orcid":false,"given":"Angelis","family":"Peteinaris","sequence":"additional","affiliation":[{"name":"Department of Urology, University of Patras Hospital, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4619-9783","authenticated-orcid":false,"given":"Lazaros","family":"Tzelves","sequence":"additional","affiliation":[{"name":"Second Department of Urology, National and Kapodistrian University of Athens, Sismanogleio Hospital, 15126 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athanasios","family":"Kostakopoulos","sequence":"additional","affiliation":[{"name":"First Department of Urology, Metropolitan General Hospital, 15562 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Skolarikos","sequence":"additional","affiliation":[{"name":"Second Department of Urology, National and Kapodistrian University of Athens, Sismanogleio Hospital, 15126 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1007\/s00345-019-03037-6","article-title":"Artificial intelligence and robotics: A combination that is changing the operating room","volume":"38","author":"Andras","year":"2020","journal-title":"World J. Urol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bellos, T., Manolitsis, I., Katsimperis, S., Julieb\u00f8-Jones, P., Feretzakis, G., Mitsogiannis, I., Varkarakis, I., Somani, B.K., and Tzelves, L. (2024). Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review. Cancers, 16.","DOI":"10.3390\/cancers16091775"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s00345-021-03738-x","article-title":"Machine learning applications to enhance patient specific care for urologic surgery","volume":"40","author":"Doyle","year":"2022","journal-title":"World J. Urol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"202","DOI":"10.4111\/icu.20230435","article-title":"Applications of artificial intelligence in urologic oncology","volume":"65","author":"Pak","year":"2024","journal-title":"Investig. Clin. Urol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1089\/end.2023.0695","article-title":"From Diagnosis to Precision Surgery: The Transformative Role of Artificial Intelligence in Urologic Imaging","volume":"38","author":"Khizir","year":"2024","journal-title":"J. Endourol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1007\/s00464-021-08792-5","article-title":"Review of automated performance metrics to assess surgical technical skills in robot-assisted laparoscopy","volume":"36","author":"Guerin","year":"2022","journal-title":"Surg. Endosc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.urology.2018.10.025","article-title":"Single-Port Robot-Assisted Radical Prostatectomy: First Clinical Experience Using the SP Surgical System","volume":"124","author":"Kaouk","year":"2019","journal-title":"Urology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5172","DOI":"10.1007\/s00464-020-07823-x","article-title":"5G ultra-remote robot-assisted laparoscopic surgery in China","volume":"34","author":"Zheng","year":"2020","journal-title":"Surg. Endosc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11571-020-09587-5","article-title":"Detecting prostate cancer using deep learning convolution neural network with transfer learning approach","volume":"14","author":"Abbasi","year":"2020","journal-title":"Cogn. Neurodynamics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6497","DOI":"10.1088\/1361-6560\/aa7731","article-title":"Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks","volume":"62","author":"Le","year":"2017","journal-title":"Phys. Med. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qiao, X., Gu, X., Liu, Y., Shu, X., Ai, G., Qian, S., Liu, L., He, X., and Zhang, J. (2023). MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer. Cancers, 15.","DOI":"10.3390\/cancers15184536"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5688","DOI":"10.1007\/s00330-022-08625-6","article-title":"Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer","volume":"32","author":"Zheng","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_13","first-page":"E145","article-title":"Prediction of prostate cancer by deep learning with multilayer artificial neural network","volume":"13","author":"Takeuchi","year":"2019","journal-title":"Can. Urol. Assoc. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2960","DOI":"10.1002\/mp.14855","article-title":"Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging","volume":"48","author":"Seetharaman","year":"2021","journal-title":"Med. Phys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1148\/radiol.2019190938","article-title":"Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment","volume":"293","author":"Schelb","year":"2019","journal-title":"Radiology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/bju.14397","article-title":"Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm","volume":"122","author":"Ishioka","year":"2018","journal-title":"BJU Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102155","DOI":"10.1016\/j.media.2021.102155","article-title":"End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction","volume":"73","author":"Saha","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wo\u017anicki, P., Westhoff, N., Huber, T., Riffel, P., Froelich, M.F., Gresser, E., von Hardenberg, J., M\u00fchlberg, A., Michel, M.S., and Schoenberg, S.O. (2020). Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters. Cancers, 12.","DOI":"10.3390\/cancers12071767"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.1158\/1078-0432.CCR-19-0374","article-title":"Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging","volume":"26","author":"Xi","year":"2020","journal-title":"Clin. Cancer Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Budai, B.K., Stollmayer, R., R\u00f3nasz\u00e9ki, A.D., K\u00f6rmendy, B., Zsombor, Z., Palot\u00e1s, L., Fej\u00e9r, B., Szendr\u00f5i, A., Sz\u00e9kely, E., and Maurovich-Horvat, P. (2022). Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols. Front. Med., 9.","DOI":"10.3389\/fmed.2022.974485"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1007\/s00261-019-01929-0","article-title":"Deep learning and radiomics: The utility of Google TensorFlow\u2122 Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT","volume":"44","author":"Coy","year":"2019","journal-title":"Abdom. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109666","DOI":"10.1016\/j.ejrad.2021.109666","article-title":"Application of deep learning as a noninvasive tool to differentiate muscle-invasive bladder cancer and non-muscle-invasive bladder cancer with CT","volume":"139","author":"Yang","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1002\/jmri.26327","article-title":"Quantitative Identification of Nonmuscle-Invasive and Muscle-Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis","volume":"49","author":"Xu","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sarkar, S., Min, K., Ikram, W., Tatton, R.W., Riaz, I.B., Silva, A.C., Bryce, A.H., Moore, C., Ho, T.H., and Sonpavde, G. (2023). Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach. Cancers, 15.","DOI":"10.3390\/cancers15061673"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s40644-023-00609-z","article-title":"CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: A multicenter study","volume":"23","author":"Song","year":"2023","journal-title":"Cancer Imaging"},{"key":"ref_26","first-page":"695","article-title":"Video labelling robot-assisted radical prostatectomy and the role of artificial intelligence (AI): Training a novice","volume":"17","author":"Aydin","year":"2023","journal-title":"J. Robot. Surg."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1007\/s11548-018-1881-9","article-title":"A computer vision technique for automated assessment of surgical performance using surgeons\u2019 console-feed videos","volume":"14","author":"Baghdadi","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_28","first-page":"545","article-title":"Capturing fine-grained details for video-based automation of suturing skills assessment","volume":"18","author":"Hung","year":"2023","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1089\/end.2023.0328","article-title":"Artificial Intelligence-Based Video Feedback to Improve Novice Performance on Robotic Suturing Skills: A Pilot Study","volume":"38","author":"Ma","year":"2024","journal-title":"J. Endourol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"528","DOI":"10.23736\/S2724-6051.22.04726-7","article-title":"\u201cAugmented reality\u201d applications in urology: A systematic review","volume":"74","author":"Roberts","year":"2022","journal-title":"Minerva Urol. Nephrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.euo.2021.04.001","article-title":"Three-dimensional Virtual Models\u2019 Assistance During Minimally Invasive Partial Nephrectomy Minimizes the Impairment of Kidney Function","volume":"5","author":"Amparore","year":"2022","journal-title":"Eur. Urol. Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.eururo.2019.11.024","article-title":"Three-dimensional Augmented Reality Robot-assisted Partial Nephrectomy in Case of Complex Tumours (PADUA \u2265 10): A New Intraoperative Tool Overcoming the Ultrasound Guidance","volume":"78","author":"Porpiglia","year":"2020","journal-title":"Eur. Urol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1007\/s11548-019-01980-8","article-title":"Assessment of surgical skills by using surgical navigation in robot-assisted partial nephrectomy","volume":"14","author":"Kobayashi","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1097\/JU.0000000000000709","article-title":"Surgical Navigation Improves Renal Parenchyma Volume Preservation in Robot-Assisted Partial Nephrectomy: A Propensity Score Matched Comparative Analysis","volume":"204","author":"Kobayashi","year":"2020","journal-title":"J. Urol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sica, M., Piazzolla, P., Amparore, D., Verri, P., De Cillis, S., Piramide, F., Volpi, G., Piana, A., Di Dio, M., and Alba, S. (2023). 3D Model Artificial Intelligence-Guided Automatic Augmented Reality Images during Robotic Partial Nephrectomy. Diagnostics, 13.","DOI":"10.3390\/diagnostics13223454"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.urology.2025.02.033","article-title":"Artificial Intelligence Based Augmented Reality Navigation in Minimally Invasive Partial Nephrectomy","volume":"199","author":"Shi","year":"2025","journal-title":"Urology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.urology.2018.01.028","article-title":"Augmented Reality Robot-assisted Radical Prostatectomy: Preliminary Experience","volume":"115","author":"Porpiglia","year":"2018","journal-title":"Urology"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1111\/bju.14549","article-title":"Augmented-reality robot-assisted radical prostatectomy using hyper-accuracy three-dimensional reconstruction (HA3D\u2122) technology: A radiological and pathological study","volume":"123","author":"Porpiglia","year":"2019","journal-title":"BJU Int."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.eururo.2019.03.037","article-title":"Three-dimensional Elastic Augmented-reality Robot-assisted Radical Prostatectomy Using Hyperaccuracy Three-dimensional Reconstruction Technology: A Step Further in the Identification of Capsular Involvement","volume":"76","author":"Porpiglia","year":"2019","journal-title":"Eur. Urol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rovera, G., Grimaldi, S., Oderda, M., Finessi, M., Giannini, V., Passera, R., Gontero, P., and Deandreis, D. (2023). Machine Learning CT-Based Automatic Nodal Segmentation and PET Semi-Quantification of Intraoperative (68)Ga-PSMA-11 PET\/CT Images in High-Risk Prostate Cancer: A Pilot Study. Diagnostics, 13.","DOI":"10.3390\/diagnostics13183013"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1089\/end.2021.0541","article-title":"The Role of 3D Models Obtained from Multiparametric Prostate MRI in Performing Robotic Prostatectomy","volume":"36","author":"Martini","year":"2022","journal-title":"J. Endourol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.eururo.2023.07.015","article-title":"Metaverse Surgical Planning with Three-dimensional Virtual Models for Minimally Invasive Partial Nephrectomy","volume":"85","author":"Checcucci","year":"2024","journal-title":"Eur. Urol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/j.eururo.2014.06.039","article-title":"A novel robotic system for single-port urologic surgery: First clinical investigation","volume":"66","author":"Kaouk","year":"2014","journal-title":"Eur. Urol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.urology.2022.10.001","article-title":"Single-port Extraperitoneal and Transperitoneal Radical Prostatectomy: A Multi-Institutional Propensity-Score Matched Study","volume":"171","author":"Beksac","year":"2023","journal-title":"Urology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.eururo.2020.11.042","article-title":"Comparing the Approach to Radical Prostatectomy Using the Multiport da Vinci Xi and da Vinci SP Robots: A Propensity Score Analysis of Perioperative Outcomes","volume":"79","author":"Moschovas","year":"2021","journal-title":"Eur. Urol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1089\/end.2021.0660","article-title":"Single-Port vs Multiport Robot-Assisted Radical Prostatectomy: A Propensity Score Matching Comparative Study","volume":"36","author":"Noh","year":"2022","journal-title":"J. Endourol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.urology.2021.05.022","article-title":"Single Port Transvesical Robotic Radical Prostatectomy: Initial Clinical Experience and Description of Technique","volume":"155","author":"Kaouk","year":"2021","journal-title":"Urology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.urology.2020.08.092","article-title":"Single-port Robotic Surgery Allows Same-day Discharge in Majority of Cases","volume":"148","author":"Abaza","year":"2021","journal-title":"Urology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.eururo.2020.03.031","article-title":"A Comparison of Perioperative Outcomes Between Single-port and Multiport Robot-assisted Laparoscopic Prostatectomy","volume":"77","author":"Vigneswaran","year":"2020","journal-title":"Eur. Urol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.urology.2020.08.046","article-title":"Comparison of Perioperative and Pathologic Outcomes Between Single-port and Standard Robot-assisted Radical Prostatectomy: An Analysis of a High-volume Center and the Pooled World Experience","volume":"147","author":"Huang","year":"2021","journal-title":"Urology"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1007\/s11701-022-01464-z","article-title":"Comparison of single-port and multi-port Retzius-sparing robot-assisted laparoscopic prostatectomy","volume":"17","author":"Shiang","year":"2023","journal-title":"J. Robot. Surg."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1089\/end.2023.0652","article-title":"Perioperative Complications of Single-Port and Multiport Robotic Radical Prostatectomy: A Single Institutional Comparison Analysis","volume":"38","author":"Soputro","year":"2024","journal-title":"J. Endourol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"640","DOI":"10.4103\/aja.aja_50_21","article-title":"A comparison of perioperative outcomes between extraperitoneal robotic single-port and multiport radical prostatectomy with the da Vinci Si Surgical System","volume":"23","author":"Ju","year":"2021","journal-title":"Asian J. Androl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.euf.2020.10.006","article-title":"Pure Single-site Robot-assisted Radical Prostatectomy Using Single-port Versus Multiport Robotic Radical Prostatectomy: A Single-institution Comparative Study","volume":"7","author":"Lenfant","year":"2021","journal-title":"Eur. Urol. Focus."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s11701-024-02066-7","article-title":"Comparison of single-port versus multi-port robotic assisted partial nephrectomy: A systematic review and meta-analysis of perioperative and oncological outcomes","volume":"18","author":"Hu","year":"2024","journal-title":"J. Robot. Surg."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1007\/s11701-025-02357-7","article-title":"A comparative analysis of perioperative outcomes in single-port and multi-port retroperitoneal robot-assisted partial nephrectomy: A systematic review and meta-analysis","volume":"19","author":"Lv","year":"2025","journal-title":"J. Robot. Surg."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1089\/end.2022.0115","article-title":"A Propensity-Matched Comparison of the Perioperative Outcomes Between Single-Port and Multi-Port Robotic Assisted Partial Nephrectomy: A Report from the Single Port Advanced Research Consortium (SPARC)","volume":"36","author":"Okhawere","year":"2022","journal-title":"J. Endourol."},{"key":"ref_58","first-page":"253","article-title":"Single-Port vs Multiport Robot-Assisted Partial Nephrectomy: A Meta-Analysis","volume":"38","author":"Nguyen","year":"2024","journal-title":"J. Endourol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1089\/end.2023.0553","article-title":"Postoperative Outcomes and Analgesic Requirements of Single-Port vs Multiport Robotic-Assisted Radical Cystectomy","volume":"38","author":"Fang","year":"2024","journal-title":"J. Endourol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.euf.2024.09.005","article-title":"Learning Curve for Single-port Robot-assisted Urological Surgery: Single-center Experience and Implications for Adoption","volume":"11","author":"Pellegrino","year":"2025","journal-title":"Eur. Urol. Focus."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.lers.2024.04.006","article-title":"Compare da Vinci 5 vs. previous versions of da Vinci and Sina: A review","volume":"7","author":"Asadizeidabadi","year":"2024","journal-title":"Laparosc. Endosc. Robot. Surg."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.eururo.2022.06.018","article-title":"Application of Improved Robot-assisted Laparoscopic Telesurgery with 5G Technology in Urology","volume":"83","author":"Li","year":"2023","journal-title":"Eur. Urol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e2412","DOI":"10.1002\/rcs.2412","article-title":"Application of 5G technology to conduct tele-surgical robot-assisted laparoscopic radical cystectomy","volume":"18","author":"Yang","year":"2022","journal-title":"Int. J. Med. Robot."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e13246","DOI":"10.1111\/ases.13246","article-title":"Tele-robotic distal gastrectomy with lymph node dissection on a cadaver","volume":"17","author":"Ebihara","year":"2024","journal-title":"Asian J. Endosc. Surg."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1007\/s00595-023-02784-9","article-title":"Effects of communication delay in the dual cockpit remote robotic surgery system","volume":"54","author":"Takahashi","year":"2024","journal-title":"Surg. Today"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.eururo.2025.06.018","article-title":"European Association of Urology Policy on Telesurgery","volume":"88","author":"Somani","year":"2025","journal-title":"Eur. Urol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1007\/s00345-022-03930-7","article-title":"Explainable artificial intelligence (XAI): Closing the gap between image analysis and navigation in complex invasive diagnostic procedures","volume":"40","author":"Janssen","year":"2022","journal-title":"World J. Urol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Gulum, M., Trombley, C., and Kantardzic, M. (2021, January 7\u20139). Improved Deep Learning Explanations for Prostate Lesion Classification through Grad-CAM and Saliency Map Fusion. Proceedings of the 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, Aveiro, Portugal.","DOI":"10.1109\/CBMS52027.2021.00099"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e232635","DOI":"10.1148\/radiol.232635","article-title":"Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI","volume":"312","author":"Cai","year":"2024","journal-title":"Radiology"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3755","DOI":"10.1111\/cas.16327","article-title":"Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter","volume":"115","author":"Tang","year":"2024","journal-title":"Cancer Sci."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/10\/3856\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T13:03:03Z","timestamp":1779109383000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/10\/3856"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,17]]},"references-count":70,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jcm15103856"],"URL":"https:\/\/doi.org\/10.3390\/jcm15103856","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,17]]}}}