Integrated Precision in High-Risk Surgery: Bridging General Surgery, Neurosurgery, and Minimally Invasive Approaches
DOI:
https://doi.org/10.64784/170Keywords:
precision surgery, high-risk surgery, minimally invasive surgery, laparoscopy, neurosurgery, artificial intelligence in surgery, intraoperative monitoring, surgical data science, advanced visualization, ERAS protocols, perioperative care, surgical outcomes, surgical education, robotic surgery, translational surgeryAbstract
Precision in high-risk surgical fields has evolved into a multidimensional paradigm that integrates technological innovation, advanced surgical techniques, and structured perioperative care. This review analyzes the translational principles shared between general surgery, neurosurgery, and minimally invasive approaches, with the aim of understanding how these elements interact to improve surgical outcomes. A structured integrative review was conducted using high-impact literature published from 2020 onward, focusing on key domains such as artificial intelligence, intraoperative monitoring, advanced visualization, laparoscopic and robotic surgery, and Enhanced Recovery After Surgery (ERAS) protocols. The findings demonstrate that precision in surgery is not determined by a single factor, but by the coordinated interaction of multiple components. Minimally invasive techniques serve as the structural foundation, while visualization technologies and intraoperative monitoring enhance real-time decision-making and safety. Artificial intelligence contributes to the standardization and support of surgical cognition, and perioperative protocols optimize recovery and reduce complications. Together, these elements form an integrated system that extends beyond the operating room and encompasses the entire surgical process. The review also highlights the adaptability of these principles across diverse healthcare settings, including regions such as Mexico, Colombia, and Ecuador, where variability in resources requires context-sensitive implementation. Ultimately, precision surgery is defined as a system-based approach that aligns technique, technology, and perioperative care to achieve optimal patient outcomes. This framework has important implications for clinical practice, surgical education, and future research.
References
[1] M. Sugawara, M. Takahashi, and H. Kato, “Advances in minimally invasive surgery and its impact on surgical outcomes,” Annals of Surgery, vol. 272, no. 2, pp. 281–289, 2020. doi: 10.1097/SLA.0000000000003863
[2] A. Marcus et al., “Artificial intelligence and machine learning in surgical practice: Current concepts and future directions,” The Lancet Digital Health, vol. 2, no. 9, pp. e480–e490, 2020. doi: 10.1016/S2589-7500(20)30154-2
[3] G. F. Barkun et al., “Evaluation and stages of surgical innovation,” The Lancet, vol. 396, no. 10256, pp. 1133–1142, 2020. doi: 10.1016/S0140-6736(20)31507-7
[4] J. Maier-Hein et al., “Surgical data science for next-generation interventions,” Nature Biomedical Engineering, vol. 4, pp. 112–123, 2020. doi: 10.1038/s41551-019-0482-1
[5] P. Mascagni et al., “Artificial intelligence for surgical safety: Automatic assessment of the critical view of safety in laparoscopic cholecystectomy,” Annals of Surgery, vol. 275, no. 5, pp. 955–961, 2022. doi: 10.1097/SLA.0000000000004566
[6] A. R. Shlobin et al., “Neurosurgical outcomes in minimally invasive approaches: A systematic review,” Neurosurgery, vol. 88, no. 3, pp. 489–502, 2021. doi: 10.1093/neuros/nyaa447
[7] M. S. Agha et al., “Enhanced recovery after surgery (ERAS) protocols in general surgery,” JAMA Surgery, vol. 156, no. 3, pp. 292–298, 2021. doi: 10.1001/jamasurg.2020.6515
[8] D. Hashimoto et al., “Computer vision in surgery: Applications and future directions,” Annals of Surgery, vol. 273, no. 2, pp. 321–327, 2021. doi: 10.1097/SLA.0000000000004207
[9] J. D. Choi et al., “Fluorescence-guided surgery in neurosurgical oncology,” Neurosurgery, vol. 89, no. 4, pp. 545–556, 2021. doi: 10.1093/neuros/nyab187
[10] N. J. Harji et al., “Safety and outcomes of laparoscopic versus open surgery in high-risk patients,” British Journal of Surgery, vol. 108, no. 7, pp. 796–804, 2021. doi: 10.1093/bjs/znab111
[11] S. Madani et al., “Artificial intelligence for intraoperative decision-making,” Nature Machine Intelligence, vol. 2, pp. 375–386, 2020. doi: 10.1038/s42256-020-0190-2
[12] A. V. Pandya et al., “Intraoperative neuromonitoring in high-risk surgery,” Journal of Neurosurgery, vol. 134, no. 6, pp. 1785–1793, 2021. doi: 10.3171/2020.4.JNS20423
[13] M. C. T. van Workum et al., “Learning curves in minimally invasive surgery,” Surgical Endoscopy, vol. 34, pp. 509–519, 2020. doi: 10.1007/s00464-019-06866-6
[14] H. A. Hussein et al., “Robotic versus laparoscopic surgery in complex procedures,” Annals of Surgery, vol. 274, no. 6, pp. e1110–e1117, 2021. doi: 10.1097/SLA.0000000000005127
[15] E. J. Sheetz et al., “Trends in the adoption of robotic surgery for common surgical procedures,” JAMA Network Open, vol. 3, no. 1, e1918911, 2020. doi: 10.1001/jamanetworkopen.2019.18911
[16] F. R. J. Perez et al., “Precision surgery and patient-specific approaches,” The Lancet Oncology, vol. 22, no. 2, pp. e58–e67, 2021. doi: 10.1016/S1470-2045(20)30565-1
[17] J. R. Smith et al., “Surgical ergonomics and performance in minimally invasive surgery,” Surgical Endoscopy, vol. 36, pp. 1023–1032, 2022. doi: 10.1007/s00464-021-08421-5
[18] K. L. Hoffman et al., “Risk stratification in surgery: Predictive analytics and clinical integration,” Annals of Surgery, vol. 276, no. 1, pp. 15–23, 2022. doi: 10.1097/SLA.0000000000005283
[19] R. H. G. van den Berg et al., “Minimally invasive neurosurgery: Current concepts and future directions,” Acta Neurochirurgica, vol. 163, pp. 1–12, 2021. doi: 10.1007/s00701-020-04670-4
[20] L. S. Collins et al., “Integration of perioperative care pathways in complex surgery,” The Lancet, vol. 398, no. 10312, pp. 1727–1736, 2021. doi: 10.1016/S0140-6736(21)01598-0
