Auffenberg, G. B. et al. Evaluation of patient- and surgeon-specific variations in patient-reported urinary outcomes 3 months after radical prostatectomy from a statewide improvement collaborative. JAMA Surg. 156, e206359 (2021).
Begg, C. B. et al. Variations in morbidity after radical prostatectomy. N. Engl. J. Med. 346, 1138–1144 (2002).
Fecso, A. B., Szasz, P., Kerezov, G. & Grantcharov, T. P. The effect of technical performance on patient outcomes in surgery. Ann. Surg. 265, 492–501 (2017).
Knudsen, J. E., Ghaffar, U., Ma, R. & Hung, A. J. Clinical applications of artificial intelligence in robotic surgery. J. Robot. Surg. 18, 102 (2024).
Haidegger, T. Autonomy for surgical robots: concepts and paradigms. IEEE Trans. Med. Robot. Bionics 1, 65–76 (2019).
Bates, D. W. et al. The potential of artificial intelligence to improve patient safety: a scoping review. npj Digit. Med. 4, 54 (2021).
Reznick, R. K. & MacRae, H. Teaching surgical skills — changes in the wind. N. Engl. J. Med. 355, 2664–2669 (2006).
Pedrett, R., Mascagni, P., Beldi, G., Padoy, N. & Lavanchy, J. L. Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review. Surg. Endosc. 37, 7412–7424 (2023).
Lam, K. et al. Machine learning for technical skill assessment in surgery: a systematic review. npj Digit. Med. 5, 24 (2022).
Ghodoussipour, S. et al. An objective assessment of performance during robotic partial nephrectomy: validation and correlation of automated performance metrics with intraoperative outcomes. J. Urol. 205, 1294–1302 (2021).
Lavanchy, J. L. et al. Automation of surgical skill assessment using a three-stage machine learning algorithm. Sci. Rep. 11, 5197 (2021).
Fard, M. J. et al. Automated robot-assisted surgical skill evaluation: predictive analytics approach. Int. J. Med. Robot. Comput. Assist. Surg. 14, e1850 (2018).
Hung, A. J. et al. Capturing fine-grained details for video-based automation of suturing skills assessment. Int. J. Comput. Assist. Radiol. Surg. 18, 545–552 (2023).
Rodriguez Peñaranda, N. et al. Artificial intelligence in surgical training for kidney cancer: a systematic review of the literature. Diagnostics 13, 3070 (2023).
Hung, A. J. et al. A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int. 124, 487–495 (2019).
Yilmaz, R. et al. Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation. npj Digit. Med. 5, 54 (2022).
Zhao, X. et al. Automatic recognition of surgical phase of robot-assisted radical prostatectomy based on artificial intelligence deep-learning model and its application in surgical skill evaluation: a joint study of 18 medical education centers. Surg. Endosc. 39, 5623–5635 (2025).
Nakajima, K. et al. Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model. Surg. Endosc. 38, 6347–6355 (2024).
Sato, K. et al. Objective surgical skill assessment based on automatic recognition of dissection and exposure times in robot-assisted radical prostatectomy. Langenbecks Arch. Surg. 410, 39 (2025).
Ma, R. et al. Surgical gestures as a method to quantify surgical performance and predict patient outcomes. npj Digit. Med. 5, 187 (2022).
Hung, A. J. et al. Surgeon automated performance metrics as predictors of early urinary continence recovery after robotic radical prostatectomy — a prospective bi-institutional study. Eur. Urol. Open Sci. 27, 65–72 (2021).
Hung, A. J., Chen, J. & Gill, I. S. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg. 153, 770–771 (2018).
Lee, R. S. et al. Machine learning to delineate surgeon and clinical factors that anticipate positive surgical margins after robot-assisted radical prostatectomy. J. Endourol. 36, 1192–1198 (2022).
Heard, J. R. et al. Surgical performance metrics for 1-year patient-reported outcomes after radical prostatectomy. JAMA Surg. 160, 674 (2025).
Hung, A. J. et al. Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes. J. Endourol. 32, 438–444 (2018).
Amparore, D. et al. From planning to prognosis: predicting renal function after minimally-invasive partial nephrectomy with artificial intelligence. Minerva Urol. Nephrol. 77, 401–407 (2025).
Kiyasseh, D. et al. A vision transformer for decoding surgeon activity from surgical videos. Nat. Biomed. Eng. 7, 780–796 (2023).
Batić, D., Holm, F., Özsoy, E., Czempiel, T. & Navab, N. EndoViT: pretraining vision transformers on a large collection of endoscopic images. Int. J. Comput. Assist. Radiol. Surg. 19, 1085–1091 (2024).
Haripriya, R., Khare, N. & Pandey, M. Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings. Sci. Rep. 15, 12482 (2025).
Joshi, H. & Joseph, S. Standardization and interoperability: federated learning’s impact on ehr systems and health informatics. Adv. Health Inf. Sci. Pract. 1, UBYM3803 (2025).
Teo, Z. L. et al. Federated machine learning in healthcare: a systematic review on clinical applications and technical architecture. Cell Rep. Med. 5, 101419 (2024).
Checcucci, E. et al. The impact of 3D models on positive surgical margins after robot-assisted radical prostatectomy. World J. Urol. 40, 2221–2229 (2022).
Schiavina, R. et al. Real-time augmented reality three-dimensional guided robotic radical prostatectomy: preliminary experience and evaluation of the impact on surgical planning. Eur. Urol. Focus 7, 1260–1267 (2021).
Checcucci, E. et al. Three-dimensional automatic artificial intelligence driven augmented-reality selective biopsy during nerve-sparing robot-assisted radical prostatectomy: a feasibility and accuracy study. Asian J. Urol. 10, 407–415 (2023).
Piana, A. et al. Automatic 3D augmented-reality robot-assisted partial nephrectomy using machine learning: our pioneer experience. Cancers 16, 1047 (2024).
Cheng, C., Lu, M., Zhang, Y. & Hu, X. Effect of augmented reality navigation technology on perioperative safety in partial nephrectomies: a meta-analysis and systematic review. Front. Surg. 10, 1067275 (2023).
Sica, M. et al. 3D model artificial intelligence-guided automatic augmented reality images during robotic partial nephrectomy. Diagnostics 13, 3454 (2023).
Madani, A. et al. Artificial intelligence for intraoperative guidance. Ann. Surg. 276, 363–369 (2022).
Kitaguchi, D. et al. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg. Endosc. 34, 4924–4931 (2020).
Mascagni, P. et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann. Surg. 275, 955–961 (2022).
Checcucci, E. et al. Development of bleeding artificial intelligence detector (BLAIR) system for robotic radical prostatectomy. J. Clin. Med. 12, 7355 (2023).
Zang, C. et al. Surgical phase recognition in inguinal hernia repair — AI-based confirmatory baseline and exploration of competitive models. Bioengineering 10, 654 (2023).
Moschovas, M. C. et al. First impressions of telesurgery robotic-assisted radical prostatectomy using the edge medical robotic platform. Int. Braz. J. Urol. 50, 754–763 (2024).
Ye, S. et al. 5G-remote radical prostatectomy under novel robotic systems: a prospective comparative cohort study with local surgeries. Prostate Cancer Prostatic Dis. (2025).
Singaravelu, A. et al. Clinical evaluation of real-time artificial intelligence provision of expert representation in indocyanine green fluorescence angiography during colorectal resections. Int. J. Surg. 110, 8246–8249 (2024).
Studier-Fischer, A. et al. Spectral characterization of intraoperative renal perfusion using hyperspectral imaging and artificial intelligence. Sci. Rep. 14, 17262 (2024).
Thiem, D. G. E. et al. Hyperspectral analysis for perioperative perfusion monitoring — a clinical feasibility study on free and pedicled flaps. Clin. Oral Investig. 25, 933–945 (2021).
Iftikhar, M., Saqib, M., Zareen, M. & Mumtaz, H. Artificial intelligence: revolutionizing robotic surgery: review. Ann. Med. Surg. 86, 5401–5409 (2024).
Lee, A., Baker, T. S., Bederson, J. B. & Rapoport, B. I. Levels of autonomy in FDA-cleared surgical robots: a systematic review. npj Digit. Med. 7, 103 (2024).
Lanfranco, A. R., Castellanos, A. E., Desai, J. P. & Meyers, W. C. Robotic surgery. Ann. Surg. 239, 14–21 (2004).
Ngu, J. C., Tsang, C. B. & Koh, D. C. the da Vinci Xi: a review of its capabilities, versatility, and potential role in robotic colorectal surgery. Robot. Surg. 4, 77–85 (2017).
DiMaio, S., Hanuschik, M. & Kreaden, U. in Surgical Robotics: Systems Applications and Visions (eds Rosen, J., Hannaford, B. & Satava, R. M.) 99–217 (Springer, 2011).
Celotto, F. et al. Da Vinci single-port robotic system current application and future perspective in general surgery: a scoping review. Surg. Endosc. 38, 4814–4830 (2024).
Harris, S. J. et al. The probot — an active robot for prostate resection. Proc. Inst. Mech. Eng. H 211, 317–325 (1997).
MacRae, C. & Gilling, P. How I do it: aquablation of the prostate using the AQUABEAM system. Can. J. Urol. 23, 8590–8593 (2016).
Zorn, K. C. et al. Aquablation therapy in large prostates (80–150 cc) for lower urinary tract symptoms due to benign prostatic hyperplasia: WATER II 3-year trial results. BJUI Compass 3, 130–138 (2022).
Saeidi, H. et al. Autonomous robotic laparoscopic surgery for intestinal anastomosis. Sci. Robot. 7, eabj2908 (2022).
Kim, J. W. B. et al. SRT-H: a hierarchical framework for autonomous surgery via language-conditioned imitation learning. Sci. Robot. 10, eadt5254 (2025).
Roche, M. The MAKO robotic-arm knee arthroplasty system. Arch. Orthop. Trauma Surg. 141, 2043–2047 (2021).
Taylor, R. H. et al. An image-directed robotic system for precise orthopaedic surgery. IEEE Trans. Robot. Autom. 10, 261–275 (1994).
Montés-Micó, R., Cerviño, A. & Ferrer-Blasco, T. VisuMax femtosecond laser for corneal refractive surgery. Expert Rev. Ophthalmol. 3, 385–388 (2008).
Kilby, W., Dooley, J. R., Kuduvalli, G., Sayeh, S. & Maurer, C. R. The CyberKnife Robotic Radiosurgery System in 2010. Technol. Cancer Res. Treat. 9, 433–452 (2010).
Scheikl, P. M. et al. LapGym — an open source framework for reinforcement learning in robot-assisted laparoscopic surgery. J. Mach. Learn. Res. 24, 1–42 (2023).
Ou, Y. & Tavakoli, M. CRESSim–MPM: a material point method library for surgical soft body simulation with cutting and suturing. In 2025 IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) 11247–11254 (IEEE, 2025).
Kim, J. W. et al. Surgical robot transformer (SRt): imitation learning for surgical tasks. In Proc. 8th Conf. Robot Learn. (eds Agrawal, P., Kroemer, O. & Burgard, W.) 130–144 (PMLR, 2024).
Rajeswaran, A. et al. Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. In Proc. Robot. Sci. Syst. XIV (RSS, 2018).
Long, Y. et al. Surgical embodied intelligence for generalized task autonomy in laparoscopic robot-assisted surgery. Sci. Robot. 10, eadt3093 (2025).
Brohan, A. et al. RT-2: Vision-language-action models transfer web knowledge to robotic control. In Proc. 7th Conf. Robot. Learn. (eds Tan, J., Toussaint, M. & Darvish, K.) 2165–2183 (PMLR, 2023).
Octo Model Team et al. Octo: an open-source generalist robot policy. In Proc. Robot. Sci. Syst. XIX (RSS, 2024).
Black, K. et al. π0.5: a vision-language-action model with open-world generalization. In Proc. 9th Conf. Robot. Learn. (eds Lim, J., Song, S. & Park, H.-W.) 17–40 (PMLR, 2025).
Cacciamani, G. E., Chen, A., Gill, I. S. & Hung, A. J. Artificial intelligence and urology: ethical considerations for urologists and patients. Nat. Rev. Urol. 21, 50–59 (2024).
World Health Organization. WHO Guidelines for Safe Surgery 2009: Safe Surgery Saves Lives (WHO, 2009).
European Council. The General Data Protection Regulation. EC https://www.consilium.europa.eu/en/policies/data-protection/data-protection-regulation (2022).
Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305–311 (2020).
Centers for Disease Control and Prevention. Health Insurance Portability and Accountability Act of 1996 (HIPAA) (CDC, 1996).
Torrent-Sellens, J., Jiménez-Zarco, A. I. & Saigí-Rubió, F. Do people trust in robot-assisted surgery? Evidence from europe. Int. J. Environ. Res. Public Health 18, 12519 (2021).
Shortliffe, E. H. & Sepúlveda, M. J. Clinical decision support in the era of artificial intelligence. JAMA 320, 2199 (2018).
Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
Adegbesan, A. et al. From scalpels to algorithms: the risk of dependence on artificial intelligence in surgery. J. Med. Surg. Public Health 3, 100140 (2024).
Natali, C., Marconi, L., Dias Duran, L. D. & Cabitza, F. AI-induced deskilling in medicine: a mixed-method review and research agenda for healthcare and beyond. Artif. Intell. Rev. 58, 356 (2025).
Vasey, B. et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat. Med. 28, 924–933 (2022).
Collins, G. S. et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11, e048008 (2021).
Cruz Rivera, S. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat. Med. 26, 1351–1363 (2020).
Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit. Health 2, e537–e548 (2020).
Kim, J. et al. Disparities in the receipt of robot-assisted radical prostatectomy: between-hospital and within-hospital analysis using 2009–2011 California inpatient data. BMJ Open 5, e007409 (2015).
Najdawi, F., Lassiter, S., Gandrabur, A., Dobbs, R. W. & Shahait, M. Impact of social determinants of health on post-operative outcomes following robotic radical prostatectomy. Curr. Oncol. Rep. 27, 1401–1408 (2025).
US Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices (FDA, 2022).
Benjamens, S., Dhunnoo, P. & Meskó, B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. npj Digit. Med. 3, 118 (2020).
World Health Organization. Ethics and Governance of Artificial Intelligence for Health: WHO Guidance (WHO, 2021).
Gerke, S., Minssen, T. & Cohen, G. in Artificial Intelligence in Healthcare Ch. 12 (Bohr, A. & Memarzadeh, K.) 295–336 (Academic, 2020).
Chen, I. Y., Szolovits, P. & Ghassemi, M. Can AI help reduce disparities in general medical and mental health care? AMA J. Ethics 21, E167–E179 (2019).
Parikh, R. B., Teeple, S. & Navathe, A. S. Addressing bias in artificial intelligence in health care. JAMA 322, 2377 (2019).
Yu, K.-H. & Kohane, I. S. Framing the challenges of artificial intelligence in medicine. BMJ Qual. Saf. 28, 238–241 (2019).