EDITORIAL
ARTIFICIAL INTELLIGENCE AND ROBOTICS IN PEDIATRIC SURGERY: EMERGING CHALLENGES AND OPPORTUNITIES FOR PEDIATRIC ANESTHESIOLOGY

UDK: 616-089-053.2:004.8616-089-053.2:007.52

www.doi.org/10.67214/fx2z2n26

Risteski T.

1University Clinic of Pediatric Surgery, Faculty of Medicine, Ss. Cyril and Methodius University, Skopje, R. North Macedonia

The integration of artificial intelligence (AI) and robotic technologies into modern healthcare represents a transformative shift in surgical and perioperative medicine. Over the past decade, these innovations have evolved from experimental concepts into clinically relevant tools, significantly influencing surgical precision, workflow optimization, and patient outcomes (1,2,4,5). In pediatric surgery, where anatomical limitations, physiological variability, and strict safety requirements are central to clinical practice, the adoption of these technologies is particularly significant. Importantly, this transformation extends beyond the surgeon’s domain and has profound implications for anesthesiology, redefining perioperative management and the role of the pediatric anesthesiologist (4,5,12).

The contemporary pediatric operating room is no longer solely dependent on manual expertise and clinical intuition.

It is increasingly becoming a technologically advanced environment characterized by robotic platforms, intelligent monitoring systems, and data-driven decision-making tools (4,12). Within this evolving ecosystem, anesthesiologists must not only maintain physiological stability but also interpret complex data outputs and adapt to new intraoperative dynamics. This shift requires a redefinition of traditional roles and highlights the growing importance of interdisciplinary collaboration.

The Changing Surgical Environment

Robotic-assisted surgery has become an important component of minimally invasive pediatric procedures. Systems such as the da Vinci Surgical System offer enhanced dexterity, tremor elimination, and superior three-dimensional visualization, enabling surgeons to perform highly precise interventions in confined anatomical spaces (1,8,9). These advantages are particularly beneficial in pediatric patients, where surgical margins are narrow and tissue handling must be meticulous.

However, the use of robotic systems significantly alters the intraoperative setting. The presence of robotic arms can limit direct access to the patient, complicating airway management and vascular access. Fixed positioning required for robotic procedures may contribute to physiological stress, especially in neonates and infants. Furthermore, the effects of pneumoperitoneum, including increased intra-abdominal pressure and carbon dioxide absorption, can influence respiratory mechanics and cardiovascular stability (3,14).

From an anesthesiology perspective, these factors necessitate careful preoperative planning and continuous intraoperative vigilance. Securing the airway and ensuring reliable intravenous access prior to docking the robotic system are essential steps.

Additionally, anesthesiologists must be prepared to manage rapid physiological changes in an environment where immediate physical access to the patient may be restricted (3).

References:

  1. Jacobson JC, Pandya SR. Pediatric robotic surgery: An overview. Semin Pediatr Surg. 2023 Feb;32(1):151255. doi: 10.1016/j.sempedsurg.2023.151255. Epub 2023 Jan 26. PMID: 36736161.
  2. Verhoeven R, Hulscher JBF. Editorial: Artificial intelligence and machine learning in pediatric surgery. Front Pediatr. 2024 Apr 9;12:1404600. doi: 10.3389/fped.2024.1404600. PMID: 38659697; PMCID: PMC11042026.
  3. Abraham AS, Gupta S. Anesthetic challenges in pediatric robot-assisted surgeries. Saudi J Anaesth. 2024 Oct-Dec;18(4):587-589. doi: 10.4103/sja.sja_330_24.
  4. Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020 Feb;132(2):379-394. doi: 10.1097/ALN.0000000000002960.
  5. Yoon HK, Yang HL, Jung CW, Lee HC. Artificial intelligence in perioperative medicine: a narrative review. Korean J Anesthesiol. 2022 Jun;75(3):202-215. doi: 10.4097/kja.22157.
  6. Kendale S, Kulkarni P, Rosenberg AD, Wang J. Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension. Anesthesiology. 2018 Oct;129(4):675-688. doi: 10.1097/ALN.0000000000002374.
  7. Disma N, Habre W. Postoperative respiratory complications in children: from prediction to clinical action. Br J Anaesth. 2025 Jan;134(1):30-31. doi: 10.1016/j.bja.2024.10.001.
  8. Sutyak KM, Tsao K. Innovations and progress in robotic pediatric general surgery. J Pediatr Surg Open. 2024;1:100041.
  9. Mattioli G, Rotondi G, Avanzini S, et al. Advancements and outcomes of robotic-assisted surgery in pediatric patients: a multicenter analysis. Front Pediatr. 2025 Sep 8;13:1652840. doi: 10.3389/fped.2025.1652840
  10. Tsai AY, Carter SR, Greene AC. Artificial intelligence in pediatric surgery. Semin Pediatr Surg. 2024 Feb;33(1):151390. doi: 10.1016/j.sempedsurg.2024.151390.
  11. Felippe VA, Dias HS, da Hora DAB, et al. Closed-loop systems for automated hypnotic drug delivery during general anaesthesia: a systematic review and meta-analysis. Br J Anaesth. 2026 Jun;136(6):1811-1821. doi: 10.1016/j.bja.2026.03.020.
  12. Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, Rashidi P, Upchurch GR Jr, Bihorac A. Artificial Intelligence and Surgical Decision-making. JAMA Surg. 2020 Feb 1;155(2):148-158. doi: 10.1001/jamasurg.2019.4917.
  13. Cheda D, Kakinuma T, Fujita S,et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024 Jan;42(1):3-15. doi: 10.1007/s11604-023-01474-3
  14. Krieger A, Opherman J., Kim J., Krieger A. Autonomous robotic surgery: readiness and ethical implications. Nat Rev Bioeng. 2025;3:112-125.
  15. Walsh JL. Artificial intelligence in pediatric perioperative monitoring. Curr Opin Anaesthesiol. 2026;39(1):45-52.