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).

Article  PubMed  PubMed Central  Google Scholar 

Begg, C. B. et al. Variations in morbidity after radical prostatectomy. N. Engl. J. Med. 346, 1138–1144 (2002).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

Knudsen, J. E., Ghaffar, U., Ma, R. & Hung, A. J. Clinical applications of artificial intelligence in robotic surgery. J. Robot. Surg. 18, 102 (2024).

Article  PubMed  PubMed Central  Google Scholar 

Haidegger, T. Autonomy for surgical robots: concepts and paradigms. IEEE Trans. Med. Robot. Bionics 1, 65–76 (2019).

Article  Google Scholar 

Bates, D. W. et al. The potential of artificial intelligence to improve patient safety: a scoping review. npj Digit. Med. 4, 54 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Reznick, R. K. & MacRae, H. Teaching surgical skills — changes in the wind. N. Engl. J. Med. 355, 2664–2669 (2006).

Article  CAS  PubMed  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Lam, K. et al. Machine learning for technical skill assessment in surgery: a systematic review. npj Digit. Med. 5, 24 (2022).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  Google Scholar 

Lavanchy, J. L. et al. Automation of surgical skill assessment using a three-stage machine learning algorithm. Sci. Rep. 11, 5197 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Fard, M. J. et al. Automated robot-assisted surgical skill evaluation: predictive analytics approach. Int. J. Med. Robot. Comput. Assist. Surg. 14, e1850 (2018).

Article  Google Scholar 

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).

PubMed  Google Scholar 

Rodriguez Peñaranda, N. et al. Artificial intelligence in surgical training for kidney cancer: a systematic review of the literature. Diagnostics 13, 3070 (2023).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Yilmaz, R. et al. Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation. npj Digit. Med. 5, 54 (2022).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Ma, R. et al. Surgical gestures as a method to quantify surgical performance and predict patient outcomes. npj Digit. Med. 5, 187 (2022).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Heard, J. R. et al. Surgical performance metrics for 1-year patient-reported outcomes after radical prostatectomy. JAMA Surg. 160, 674 (2025).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

Kiyasseh, D. et al. A vision transformer for decoding surgeon activity from surgical videos. Nat. Biomed. Eng. 7, 780–796 (2023).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Haripriya, R., Khare, N. & Pandey, M. Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings. Sci. Rep. 15, 12482 (2025).

Article  CAS  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

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.