EC Gynaecology

Background: Robotic-assisted surgery represents the most technologically advanced modality of Minimally Invasive Gynecological Surgery (MIGS). The integration of Artificial Intelligence (AI) with robotic surgical platforms including the established da Vinci system and emerging platforms such as the Hugo RAS system offers unprecedented opportunities to enhance surgical precision, automate complex tasks, and provide real-time decision support. However, a focused synthesis of AI applications specifically within robotic gynecological surgery, encompassing both clinical outcomes and technological trajectories, is lacking.

Objective: To provide a focused narrative review of AI applications within robotic-assisted gynecological surgery, examining the current evidence on clinical outcomes, specific AI-enabled capabilities, emerging robotic platforms, and future technological directions.

Methods: A focused narrative review was conducted, drawing on evidence from a comprehensive systematic review of AI in MIGS (55 included studies, 48,951 patients) and supplementary targeted literature searches for robotic-specific AI applications in gynecology.

Results: AI-enhanced robotic gynecological surgery encompasses four principal domains: (1) intraoperative guidance and anatomical navigation; (2) surgical skill assessment and training; (3) predictive modeling for personalized surgical planning; and (4) semi-autonomous task execution. The da Vinci platform remains the dominant robotic system, with AI enhancements including Firefly near-infrared imaging and emerging computer vision modules. The Hugo RAS system represents a new generation of AI-integrated robotic platforms with promising early clinical results. Meta-analysis data from the broader systematic review demonstrate that robotic AI-assisted MIGS is associated with the greatest reductions in operative time (MD: -28.4 min) and blood loss (MD: -48.3 mL for myomectomy) compared to conventional approaches.

Conclusion: The convergence of AI and robotic surgery in gynecology is advancing rapidly, with current applications in decision support and performance analytics yielding measurable clinical benefits. The trajectory toward greater surgical autonomy is clear, but requires parallel development of ethical frameworks, regulatory standards, and training curricula to ensure safe and equitable implementation.

Keywords: Artificial Intelligence; Robotic Surgery; Gynecology; da Vinci; Hugo RAS; Autonomous Surgery; Surgical Outcomes; Machine Learning; Computer Vision; Surgical Training

1. Moglia A., et al. “A systematic review on artificial intelligence in robot-assisted surgery”. International Journal of Surgery 95 (2021): 106151.
2. Lenfant L., et al. “Robotic-assisted benign hysterectomy compared with laparoscopic, vaginal, and open surgery: a systematic review and meta-analysis”. Journal of Robotic Surgery6 (2023): 2647-2662.
3. Wah JNK. “Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation”. Journal of Robotic Surgery1 (2025): 47.
4. Pavone M., et al. “Unveiling the real benefits of robot-assisted surgery in gynaecology: from telesurgery to image-guided surgery and artificial intelligence”. Facts, Views and Vision in ObGyn 1 (2025): 50-60.
5. Innocenzi C., et al. “The Hugo RAS system in gynecologic robotic surgery: a systematic review of current applications”. Journal of Robotic Surgery1 (2025): 22.
6. Pipes GM., et al. “Artificial intelligence and gynecologic surgery”. Obstetrics and Gynecology 5 (2025): 672-678.
7. Dou Q., et al. “Artificial intelligence in gynecology surgery: Current status, challenges, and future opportunities”. Chinese Medical Journal 3 (2025): 631-633.
8. Collins T., et al. “Augmented reality guided laparoscopic surgery of the uterus”. IEEE Transactions on Medical Imaging 1 (2021): 371-380.
9. Zhao Y. “Artificial intelligence for endometriosis diagnosis and treatment”. Human Reproduction Update (2025).
10. Chen S., et al. “Initial results in the automatic visual recognition of endometriosis lesions by artificial intelligence during laparoscopy”. Journal of Minimally Invasive Gynecology 12 (2026): 1118-1125.
11. Lam K., et al. “Machine learning for technical skill assessment in surgery: a systematic review”. npj Digital Medicine 5 (2022): 24.
12. Kankanamge D., et al. “Artificial intelligence based assessment of minimally invasive surgical skills using standardised objective metrics - A narrative review”. American Journal of Surgery 241 (2025): 116074.
13. Khalid S., et al. “Evaluation of deep learning models for identifying surgical actions and measuring performance”. JAMA Network Open3 (2020): e201664.
14. Levin I., et al. “Routine automated assessment using surgical intelligence reveals substantial time spent outside the patient's body in minimally invasive gynecological surgeries”. Journal of Minimally Invasive Gynecology 10 (2024): 843-846.
15. Tinelli A., et al. “Artificial intelligence and uterine fibroids: A useful combination for diagnosis and treatment”. Journal of Clinical Medicine 10 (2025): 3454.
16. Walczak S and Mikhail E. “Predicting estimated blood loss and transfusions in gynecologic surgery using artificial neural networks”. International Journal of Healthcare Information Systems and Informatics 1 (2021): 1-15.
17. Saadati S and Amirmazlaghani M. “Revolutionizing endometriosis treatment: automated surgical operation through artificial intelligence and robotic vision”. Journal of Robotic Surgery 1 (2024): 383.
18. Wah JNK. “AI-powered robotic surgery: transforming surgical decisions”. Journal of Robotic Surgery 1 (2025): 94.
19. Collins JW., et al. “Ethical implications of AI in robotic surgical training: A Delphi consensus statement”. European Urology Focus 2 (2022): 613-622.
20. Park J., et al. “Artificial intelligence in surgical simulation and training”. Surgical Endoscopy (2022).