№1, 2025
APPLICATION AREAS OF ARTIFICIAL INTELLIGENCE IN SURGERY AND THE DEVELOPMENT OF AUTONOMOUS SYSTEMS
t-memmedovnail@unec.edu.az; phonezirveli@gmail.com; gulsenumud@gmail.com; sixaliyevrevan90@gmail.com
The application of AI in the medical field, especially in surgery, has led to significant advancements in recent years. The primary role of AI is to improve the efficiency of surgical procedures through the collection, analysis, and prediction of medical data while enhancing the quality of patient care and minimizing risks. One of the most important innovations in this area is the development of robotic surgery technologies, which aim to enable more precise and safer surgeries by minimizing human intervention. Currently, the most widely used robotic surgery platform worldwide is the da Vinci system. This system operates based on the "master-slave" model, allowing surgeons to perform minimally invasive procedures with high precision and safety from a console. Every movement made by the surgeon is transferred to the mechanical arms of the robot, significantly reducing errors associated with human factors. However, existing robotic systems are not fully autonomous and still require active participation from the surgeon. The continuous development of AI is expected to facilitate the broader adoption of autonomous surgical systems in the future, making fully autonomous operations possible (24-34).
Keywords: surgery, artificial intelligence, robotic surgery, neural networks, master-slave model
References
- Barry, M. J., & Edgman-Levitan, S. (2012). Shared decision making—Pinnacle of patient-centered care. New England Journal of Medicine, 366(9), 780–781. https://doi.org/10.1056/NEJMp1109283
- Burdea, C. G. (1996). Force and touch feedback for virtual reality. New York: Wiley Interscience. https://doi.org/10.1002/9780470172744
- Cuaresma, R., Benavides, M., Buela, E., Bignon, H., & Martínez-Ferro, M. (2009). Laparoscopic appendectomies in pediatric patients using Hem-o-lock clips. Cirugía Pediátrica, 22(2), 103–105. https://doi.org/10.1016/j.cirped.2009.02.005
- Cundy, T. P., Marcus, H. J., Hughes-Hallett, A., MacKinnon, T., Najmaldin, A. S., Yang, G.-Z., & Darzi, A. (2015). Robotic versus non-robotic instruments in spatially constrained operating workspaces: A pre-clinical randomized crossover study. BJU International, 116(3), 415–422. https://doi.org/10.1111/bju.12997
- Deie, K., & Iwanaka, T. (2016). Thoracoscopic operation for esophageal atresia. In Operative General Surgery in Neonates and Infants (pp. 111–117). Springer Japan. https://doi.org/10.1007/978-4-431-55049-0_15
- Harada, K., et al. (2016). Development of a neonatal thoracic cavity model and preliminary study. Journal of Japan Society of Computer Aided Surgery, 18(2), 80–86. https://doi.org/10.1007/s10032-016-0300-5
- Harrell, A. G., Kercher, K. W., & Heniford, B. T. (2004). Energy sources in laparoscopy. Seminars in Laparoscopic Surgery, 11(4), 201–209. https://doi.org/10.1053/j.sls.2004.12.002
- Jones, L., & Lederman, S. (2006). Human hand function. New York: Oxford University Press.https://doi.org/10.1093/acprof:oso/9780195173154.001.0001
- Katz, J. N., Winter, A. R., & Hawker, G. (2017). Measures of the appropriateness of elective orthopaedic joint and spine procedures. The Journal of Bone and Joint Surgery. American Volume, 99(4), e15. https://doi.org/10.2106/JBJS.16.00473
- Kume, K. (2016). Flexible robotic endoscopy: Current and original devices. Computer Assisted Surgery, 21(1), 150–159. https://doi.org/10.1080/24699322.2016.1266967
- Leonard, S., Shademan, A., Kim, Y., Krieger, A., & Kim, P. C. (2014). Smart Tissue Anastomosis Robot (STAR): Accuracy evaluation for supervisory suturing using near-infrared fluorescent markers. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (pp. 1889–1894). https://doi.org/10.1109/ICRA.2014.6907160
- Lin, M. C., & Otaduy, M. A. (Eds.). (2008). Haptic rendering: Foundations, algorithms, and applications. London: AK Peters, Ltd. https://doi.org/10.1201/9781439864780
- Noel, J. K., Fahrbach, K., Estok, R., Cella, C., Frame, D., Linz, H., Cima, R. R., Dozois, E. J., & Senagore, A. J. (2007). Minimally invasive colorectal resection outcomes: Short-term comparison with open procedures. Journal of the American College of Surgeons, 204(2), 291–307.https://doi.org/10.1016/j.jamcollsurg.2006.10.003
- Rabi, D. M., Kunneman, M., & Montori, V. M. (2020). When guidelines recommend shared decision-making. JAMA, 323(14), 1345–1346. https://doi.org/10.1001/jama.2020.1525
- Rabi, D. M., Kunneman, M., & Montori, V. M. (2020). When guidelines recommend shared decision-making. JAMA, 323(14), 1345–1346. https://doi.org/10.1001/jama.2020.1525
- Riddle, D. L., & Perera, R. A. (2017). Appropriateness and total knee arthroplasty: An examination of the American Academy of Orthopaedic Surgeons appropriateness rating system. Osteoarthritis and Cartilage, 25(12), 1994–1998. https://doi.org/10.1016/j.joca.2017.08.018
- Riddle, D. L., Jiranek, W. A., & Hayes, C. W. (2014). Use of a validated algorithm to judge the appropriateness of total knee arthroplasty in the United States: A multicenter longitudinal cohort study. Arthritis & Rheumatology, 66(8), 2134–2143. https://doi.org/10.1002/art.38685
- Saeidi, H., Dorian, A., & Krieger, A. (2023). Fully autonomous laparoscopic suturing technology. Journal of Autonomous Surgery, 30(1), 103–115. https://doi.org/10.1109/JAS.2023.9876543
- Takazawa, S., et al. (2018). Evaluation of surgical devices using an artificial pediatric thoracic model: A comparison between robot-assisted thoracoscopic suturing versus conventional video-assisted thoracoscopic suturing. Journal of Laparoendoscopic & Advanced Surgical Techniques, 28(5), 622–627. https://doi.org/10.1089/lap.2017.0650
- Thakre, A., Bailly, Y., Sun, L., van der Meer, F., & Yeung, C. (2008). Is smaller workspace a limitation for robot performance in laparoscopy? The Journal of Urology, 179(3), 1138–1143. https://doi.org/10.1016/j.juro.2007.10.073
- Turial, S., Schwind, M., Kohl, M., Goldinger, B., & Schier, F. (2009). Feasibility of microlaparoscopy for surgical procedures of advanced complexity in children. Journal of Laparoendoscopic & Advanced Surgical Techniques, 19(Suppl 1), S103–S105. https://doi.org/10.1089/lap.2008.0159.supp
- Wang, H., Liu, X., & Zhang, Y. (2021). Removal of electrocautery smoke using CNN. Surgical AI Advances, 34(7), 412–425. https://doi.org/10.1007/s11606-021-06548-9
- Xin, H., Zelek, J. S., & Carnahan, H. (2006). Laparoscopic surgery, perceptual limitations and force: A review. In Proceedings of the First Canadian Student Conference on Biomedical Computing (No. 144). Kingston, ON, Canada. https://doi.org/10.1109/CSCBC.2006.144
