Abstract
Background
Digital surgery is a new paradigm within the surgical innovation space that is rapidly advancing and encompasses multiple areas.
Methods
This white paper from the SAGES Digital Surgery Working Group outlines the scope of digital surgery, defines key terms, and analyzes the challenges and opportunities surrounding this disruptive technology.
Results
In its simplest form, digital surgery inserts a computer interface between surgeon and patient. We divide the digital surgery space into the following elements: advanced visualization, enhanced instrumentation, data capture, data analytics with artificial intelligence/machine learning, connectivity via telepresence, and robotic surgical platforms. We will define each area, describe specific terminology, review current advances as well as discuss limitations and opportunities for future growth.
Conclusion
Digital Surgery will continue to evolve and has great potential to bring value to all levels of the healthcare system. The surgical community has an essential role in understanding, developing, and guiding this emerging field.
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References
Atallah S (ed) (2020) Digital surgery, 1st edn. Springer, Cham
Guanà R, Ferrero L, Garofalo S, Cerrina A, Cussa D, Arezzo A et al (2017) Skills comparison in pediatric residents using a 2-dimensional versus a 3-dimensional high-definition camera in a pediatric laparoscopic simulator. J Surg Educ 74(4):644–649. https://doi.org/10.1016/j.jsurg.2016.12.002
Tanagho YS, Andriole GL, Paradis AG, Madison KM, Sandhu GS, Varela JE et al (2012) 2D versus 3D visualization: impact on laparoscopic proficiency using the fundamentals of laparoscopic surgery skill set. J Laparoendosc Adv Surg Tech A 22(9):865–870. https://doi.org/10.1089/lap.2012.0220
Reinhart MB, Huntington CR, Blair LJ, Heniford BT, Augenstein VA (2016) Indocyanine green: historical context, current applications, and future considerations. Surg Innov 23(2):166–175. https://doi.org/10.1177/1553350615604053
Broderick RC, Lee AM, Cheverie JN, Zhao B, Blitzer RR, Patel RJ et al (2021) Fluorescent cholangiography significantly improves patient outcomes for laparoscopic cholecystectomy. Surg Endosc 35(10):5729–5739. https://doi.org/10.1007/s00464-020-08045-x
Blanco-Colino R, Espin-Basany E (2018) Intraoperative use of ICG fluorescence imaging to reduce the risk of anastomotic leakage in colorectal surgery: a systematic review and meta-analysis. Tech Coloproctol 22(1):15–23. https://doi.org/10.1007/s10151-017-1731-8
Gupta A, Ruijters D, Flexman ML (2021) Augmented reality for interventional procedures. Digital surgery. Springer, Cham
Shuhaiber JH (2004) Augmented reality in surgery. Arch Surg 139(2):170–174. https://doi.org/10.1001/archsurg.139.2.170
Ajstefansic JH (1999) Image-guided surgery: preliminary feasibility studies of frameless stereotactic liver surgery. Arch Surg 134(6):644–650
Mountney P, Yang G-Z (2010) Motion compensated SLAM for image guided surgery. Med Image Comput Comput Assist Interv 13(Pt 2):496–504. https://doi.org/10.1007/978-3-642-15745-5_61
Bernhardt S, Nicolau SA, Soler L, Doignon C (2017) The status of augmented reality in laparoscopic surgery as of 2016. Med Image Anal 37:66–90. https://doi.org/10.1016/j.media.2017.01.007
Gmeiner M, Dirnberger J, Fenz W, Gollwitzer M, Wurm G, Trenkler J et al (2018) Virtual cerebral aneurysm clipping with real-time haptic force feedback in neurosurgical education. World Neurosurg 112:e313–e323. https://doi.org/10.1016/j.wneu.2018.01.042
Gsaxner C (2021) Augmented reality in oral and maxillofacial surgery. Computer-aided oral and maxillofacial surgery. Academic Press, Cambridge
Kang X, Azizian M, Wilson E, Wu K, Martin AD, Kane TD et al (2014) Stereoscopic augmented reality for laparoscopic surgery. Surg Endosc 28(7):2227–2235. https://doi.org/10.1007/s00464-014-3433-x
Xin B, Huang X, Wan W, Lv K, Hu Y, Wang J et al (2020) The efficacy of immersive virtual reality surgical simulator training for pedicle screw placement: a randomized double-blind controlled trial. Int Orthop 44(5):927–934. https://doi.org/10.1007/s00264-020-04488-y
Gavaghan KA, Peterhans M, Oliveira-Santos T, Weber S (2011) A portable image overlay projection device for computer-aided open liver surgery. IEEE Trans Biomed Eng 58(6):1855–1864. https://doi.org/10.1109/TBME.2011.2126572
Reitinger B, Bornik A, Beichel R, Schmalstieg D (2006) Liver surgery planning using virtual reality. IEEE Comput Graph Appl 26(6):36–47. https://doi.org/10.1109/mcg.2006.131
Wendler T, Herrmann K, Schnelzer A, Lasser T, Traub J, Kutter O et al (2010) First demonstration of 3-D lymphatic mapping in breast cancer using freehand SPECT. Eur J Nucl Med Mol Imaging 37(8):1452–1461. https://doi.org/10.1007/s00259-010-1430-4
RSIP Vision (2023) RSIP Neph announces a revolutionary intra-op solution for partial nephrectomy surgeries. PR Newswire. https://www.prnewswire.com/news-releases/rsip-neph-announces-a-revolutionary-intra-op-solution-for-partial-nephrectomy-surgeries-301731484.html. Accessed 19 Mar 2023
Shah SK, Nwaiwu CA, Agarwal A, Bajwa KS, Felinski M, Walker PA et al (2021) First-in-human (FIH) safety, feasibility, and usability trial of a laparoscopic imaging device using laser speckle contrast imaging (LSCI) visualizing real-time tissue perfusion and blood flow without fluorophore in colorectal and bariatric patients. J Am Coll Surg 233(5):S45–S46. https://doi.org/10.1016/j.jamcollsurg.2021.07.070
Vávra P, Roman J, Zonča P, Ihnát P, Němec M, Kumar J et al (2017) Recent development of augmented reality in surgery: a review. J Healthc Eng 2017:1–9. https://doi.org/10.1155/2017/4574172
Pratt P, Stoyanov D, Visentini-Scarzanella M, Yang G-Z (2010) Dynamic guidance for robotic surgery using image-constrained biomechanical models. Med Image Comput Comput Assist Interv 13(Pt 1):77–85. https://doi.org/10.1007/978-3-642-15705-9_10
Condino S, Carbone M, Piazza R, Ferrari M, Ferrari V (2020) Perceptual limits of optical see-through visors for augmented reality guidance of manual tasks. IEEE Trans Biomed Eng 67(2):411–419. https://doi.org/10.1109/TBME.2019.2914517
Edwards PJ, Chand M, Birlo M, Stoyanov D (2021) The challenge of augmented reality in surgery. In: Digital surgery. Springer, Cham, pp 121–135
Dario P, Hannaford B, Menciassi A (2003) Smart surgical tools and augmenting devices. IEEE Trans Rob Autom 19(5):782–792. https://doi.org/10.1109/tra.2003.817071
Gaidry AD, Tremblay L, Nakayama D, Ignacio RC Jr (2019) The history of surgical staplers: a combination of Hungarian, Russian, and American innovation. Am Surg 85(6):563–566. https://doi.org/10.1177/000313481908500617
Kim J-S, Park S-H, Kim N-S, Lee IY, Jung HS, Ahn H-M et al (2022) Compression automation of circular stapler for preventing compression injury on gastrointestinal anastomosis. Int J Med Robot 18(3):e2374. https://doi.org/10.1002/rcs.2374
Roy S, Yoo A, Yadalam S, Fegelman EJ, Kalsekar I, Johnston SS (2017) Comparison of economic and clinical outcomes between patients undergoing laparoscopic bariatric surgery with powered versus manual endoscopic surgical staplers. J Med Econ 20(4):423–433. https://doi.org/10.1080/13696998.2017.1296453
Pla-Martí V, Martín-Arévalo J, Moro-Valdezate D, García-Botello S, Mora-Oliver I, Gadea-Mateo R et al (2021) Impact of the novel powered circular stapler on risk of anastomotic leakage in colorectal anastomosis: a propensity score-matched study. Tech Coloproctol 25(3):279–284. https://doi.org/10.1007/s10151-020-02338-y
Pollack E, Johnston S, Petraiuolo WJ, Roy S, Galvain T (2021) Economic analysis of leak complications in anastomoses performed with powered versus manual circular staplers in left-sided colorectal resections: a US-based cost analysis. Clinicoecon Outcomes Res 13:531–540. https://doi.org/10.2147/ceor.s305296
Levy B, Emery L (2003) Randomized trial of suture versus electrosurgical bipolar vessel sealing in vaginal hysterectomy. Obstet Gynecol 102(1):147–151. https://doi.org/10.1016/s0029-7844(03)00405-8
Sutton C, Abbott J (2013) History of power sources in endoscopic surgery. J Minim Invasive Gynecol 20(3):271–278. https://doi.org/10.1016/j.jmig.2013.03.001
Singleton D, Chekan E, Davison M, Mennone J, Hinoul P (2015) Consistency and sealing of advanced bipolar tissue sealers. Med Devices (Auckl). https://doi.org/10.2147/mder.s79642
Eickhoff A, Van Dam J, Jakobs R, Kudis V, Hartmann D, Damian U, Weickert U, Schilling D, Riemann JF (2007) Computer-assisted colonoscopy (the neoguide endoscopy system): results of the first human clinical trial. Am J Gastroenterol 102:261–266
Rothstein DH, Raval MV (2018) Operating room efficiency. Semin Pediatr Surg 27(2):79–85. https://doi.org/10.1053/j.sempedsurg.2018.02.004
Mayer EK, Sevdalis N, Rout S, Caris J, Russ S, Mansell J et al (2016) Surgical checklist implementation project: the impact of variable WHO checklist compliance on risk-adjusted clinical outcomes after national implementation. A longitudinal study. Ann Surg 263(1):58–63. https://doi.org/10.1097/sla.0000000000001185
Russ S, Arora S, Wharton R, Wheelock A, Hull L, Sharma E et al (2013) Measuring safety and efficiency in the operating room: development and validation of a metric for evaluating task execution in the operating room. J Am Coll Surg 216(3):472–481. https://doi.org/10.1016/j.jamcollsurg.2012.12.013
Ayas S, Gordon L, Donmez B, Grantcharov T (2021) The effect of intraoperative distractions on severe technical events in laparoscopic bariatric surgery. Surg Endosc 35(8):4569–4580. https://doi.org/10.1007/s00464-020-07878-w
Jung JJ, Jüni P, Lebovic G, Grantcharov T (2020) First-year analysis of the operating room black box study. Ann Surg 271(1):122–127. https://doi.org/10.1097/sla.0000000000002863
Okamura AM (2016) ICRA lecture 2: kinematics and control of medical robots. In: ICRA 2016 Tutorial on Medical Robotics
Kuo C-H, Dai JS, Dasgupta P (2012) Kinematic design considerations for minimally invasive surgical robots: an overview: kinematic design considerations for MIS robots. Int J Med Robot 8(2):127–145. https://doi.org/10.1002/rcs.453
van Amsterdam B, Clarkson MJ, Stoyanov D (2021) Gesture recognition in robotic surgery: a review. IEEE Trans Biomed Eng 68(6):2021–2035. https://doi.org/10.1109/tbme.2021.3054828
Green CA, Kim EH, O’Sullivan PS, Chern H (2018) Using technological advances to improve surgery curriculum: experience with a mobile application. J Surg Educ 75(4):1087–1095. https://doi.org/10.1016/j.jsurg.2017.12.005
Catchpole K, Perkins C, Bresee C, Solnik MJ, Sherman B, Fritch J et al (2016) Safety, efficiency and learning curves in robotic surgery: a human factors analysis. Surg Endosc 30(9):3749–3761. https://doi.org/10.1007/s00464-015-4671-2
Weigl M, Weber J, Hallett E, Pfandler M, Schlenker B, Becker A et al (2018) Associations of intraoperative flow disruptions and operating room teamwork during robotic-assisted radical prostatectomy. Urology 114:105–113. https://doi.org/10.1016/j.urology.2017.11.060
Law KE, Ray RD, D’Angelo ALD, Cohen ER, DiMarco SM, Linsmeier E et al (2016) Exploring senior residents’ intraoperative error management strategies: a potential measure of performance improvement. J Surg Educ 73(6):e64–e70. https://doi.org/10.1016/j.jsurg.2016.05.016
Ibrahim AM, Varban OA, Dimick JB (2016) Novel uses of video to accelerate the surgical learning curve. J Laparoendosc Adv Surg Tech A 26(4):240–242. https://doi.org/10.1089/lap.2016.0100
Saun TJ, Zuo KJ, Grantcharov TP (2019) Video technologies for recording open surgery: a systematic review. Surg Innov 26(5):599–612. https://doi.org/10.1177/1553350619853099
Abdelsattar JM, Pandian TK, Finnesgard EJ, El Khatib MM, Rowse PG, Buckarma ENH et al (2015) Do you see what I see? How we use video as an adjunct to general surgery resident education. J Surg Educ 72(6):e145–e150. https://doi.org/10.1016/j.jsurg.2015.07.012
Isaacson D, Green C, Topp K, O’Sullivan P, Kim E (2017) Guided laparoscopic video tutorials for medical student instruction in abdominal anatomy. Mededportal 13(1):10559. https://doi.org/10.15766/mep_2374-8265.10559
Dominguez CO, Flach JM, McKellar DP, Dunn M (2002) Using videotaped cases to elicit perceptual expertise in laparoscopic surgery. In: Proceedings third annual symposium on human interaction with complex systems HICS’96. IEEE Computer Society Press
Mazer L, Varban O, Montgomery JR, Awad MM, Schulman A (2022) Video is better: why aren’t we using it? A mixed-methods study of the barriers to routine procedural video recording and case review. Surg Endosc 36(2):1090–1097. https://doi.org/10.1007/s00464-021-08375-4
Green JL, Suresh V, Bittar P, Ledbetter L, Mithani SK, Allori A (2019) The utilization of video technology in surgical education: a systematic review. J Surg Res 235:171–180. https://doi.org/10.1016/j.jss.2018.09.015
Hu Y-Y, Mazer LM, Yule SJ, Arriaga AF, Greenberg CC, Lipsitz SR et al (2017) Complementing operating room teaching with video-based coaching. JAMA Surg 152(4):318–325. https://doi.org/10.1001/jamasurg.2016.4619
Stulberg JJ, Huang R, Kreutzer L, Ban K, Champagne BJ, Steele SR et al (2020) Association between surgeon technical skills and patient outcomes. JAMA Surg 155(10):960–968. https://doi.org/10.1001/jamasurg.2020.3007
Grenda TR, Pradarelli JC, Dimick JB (2016) Using surgical video to improve technique and skill. Ann Surg 264(1):32–33. https://doi.org/10.1097/sla.0000000000001592
Madani A, Vassiliou MC, Watanabe Y, Al-Halabi B, Al-Rowais MS, Deckelbaum DL et al (2017) What are the principles that guide behaviors in the operating room?: Creating a framework to define and measure performance. Ann Surg 265(2):255–267. https://doi.org/10.1097/sla.0000000000001962
Hashimoto DA, Rosman G, Rus D, Meireles OR (2018) Artificial intelligence in surgery: promises and perils. Ann Surg 268(1):70–76. https://doi.org/10.1097/sla.0000000000002693
Ward TM, Mascagni P, Ban Y, Rosman G, Padoy N, Meireles O et al (2021) Computer vision in surgery. Surgery 169(5):1253–1256. https://doi.org/10.1016/j.surg.2020.10.039
Mascagni P, Alapatt D, Sestini L, Altieri MS, Madani A, Watanabe Y et al (2022) Computer vision in surgery: from potential to clinical value. NPJ Digit Med. https://doi.org/10.1038/s41746-022-00707-5
Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM et al (2020) Artificial intelligence and surgical decision-making. JAMA Surg 155(2):148–158. https://doi.org/10.1001/jamasurg.2019.4917
Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W et al (2018) Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 155(4):1069-1078.e8. https://doi.org/10.1053/j.gastro.2018.06.037
Madani A, Namazi B, Altieri MS, Hashimoto DA, Rivera AM, Pucher PH et al (2022) Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg 276(2):363–369. https://doi.org/10.1097/sla.0000000000004594
Zhang X, Chen F, Yu T, An J, Huang Z, Liu J et al (2019) Real-time gastric polyp detection using convolutional neural networks. PLoS ONE 14(3):e0214133. https://doi.org/10.1371/journal.pone.0214133
Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E (2019) Artificial Intelligence: a new tool in operating room management. Role of machine learning models in operating room optimization. J Med Syst 44(1):20. https://doi.org/10.1007/s10916-019-1512-1
Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C et al (2020) Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol 5(4):343–351. https://doi.org/10.1016/S2468-1253(19)30411-X
Mascagni P, Vardazaryan A, Alapatt D, Urade T, Emre T, Fiorillo C et al (2022) Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg 275(5):955–961. https://doi.org/10.1097/SLA.0000000000004351
Birkmeyer JD, Finks JF, O’reilly A, Oerline M, Carlin AM, Nunn AR et al (2013) Michigan bariatric surgery collaborative. Surgical skill and complication rates after bariatric surgery. N Engl J Med 369:1434–1442
Katz AJ (2016) The role of crowdsourcing in assessing surgical skills. Surg Laparosc Endosc Percutan Tech 26:271–277
Hung AJ, Chen J, Gill IS (2018) Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg. https://doi.org/10.1001/jamasurg.2018.1512
Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA (2018) Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator: derivation and validation of a novel, user-friendly, and machine-learning-based predictive OpTimal trees in emergency surgery risk (POTTER) calculator. Ann Surg 268(4):574–583. https://doi.org/10.1097/SLA.0000000000002956
Loftus TJ, Vlaar APJ, Hung AJ, Bihorac A, Dennis BM, Juillard C et al (2022) Executive summary of the artificial intelligence in surgery series. Surgery 171(5):1435–1439. https://doi.org/10.1016/j.surg.2021.10.047
Cheng J-Z, Ni D, Chou Y-H, Qin J, Tiu C-M, Chang Y-C et al (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 6(1):24454. https://doi.org/10.1038/srep24454
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118. https://doi.org/10.1038/nature21056
Donald R, Howells T, Piper I, Enblad P, Nilsson P, Chambers I et al (2019) Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care. J Clin Monit Comput 33(1):39–51. https://doi.org/10.1007/s10877-018-0139-y
CLEW (2021) CLEW Medical receives FDA clearance for AI-based predictive analytics technology to support adult ICU patient assessment. PR Newswire. https://www.prnewswire.com/il/news-releases/clew-medical-receives-fda-clearance-for-ai-based-predictive-analytics-technology-to-support-adult-icu-patient-assessment-301221173.html. Accessed 19 Mar 2023
Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P et al (2020) Effect of a machine learning-derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical trial: the HYPE randomized clinical trial. JAMA 323(11):1052–1060. https://doi.org/10.1001/jama.2020.0592
Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA (2018) The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med 24(11):1716–1720. https://doi.org/10.1038/s41591-018-0213-5
Adler JR Jr, Chang SD, Murphy MJ, Doty J, Geis P, Hancock SL (1997) The cyberknife: a frameless robotic system for radiosurgery. Stereotact Funct Neurosurg 69(1–4 Pt 2):124–128. https://doi.org/10.1159/000099863
Gonzalez GT, Kaur U, Rahman M, Venkatesh V, Sanchez N, Hager G et al (2021) From the dexterous surgical skill to the battlefield—a robotics exploratory study. Robot Explor Study 186:288–294
Murali A, Sen S, Kehoe B, Garg A, McFarland S, Patil S et al (2015) Learning by observation for surgical subtasks: multilateral cutting of 3D viscoelastic and 2D orthotropic tissue phantoms. In: 2015 IEEE international conference on robotics and automation (ICRA). IEEE
Saeidi H, Opfermann JD, Kam M, Wei S, Leonard S, Hsieh MH et al (2022) Autonomous robotic laparoscopic surgery for intestinal anastomosis. Sci Robot 7(62):eabj908. https://doi.org/10.1126/scirobotics.abj2908
Balch J, Upchurch GR Jr, Bihorac A, Loftus TJ (2021) Bridging the artificial intelligence valley of death in surgical decision-making. Surgery 169(4):746–748. https://doi.org/10.1016/j.surg.2021.01.008
Lifshitz B (2021) Racism is systemic in artificial intelligence systems, too. Georget Secur Stud Rev. https://georgetownsecuritystudiesreview.org/2021/05/06/racism-is-systemic-in-artificial-intelligence-systems-too/. Accessed 19 Mar 2023
Murthy VH, Krumholz HM, Gross CP (2004) Participation in cancer clinical trials: race-, sex-, and age-based disparities. JAMA 291(22):2720. https://doi.org/10.1001/jama.291.22.2720
CPT® Appendix S: artificial intelligence taxonomy for medical services and procedures. Ama-assn.org. 2022. https://www.ama-assn.org/system/files/cpt-appendix-s.pdf. Accessed 19 Mar 2023
Williams TE Jr, Satiani B, Thomas A, Ellison EC (2009) The impending shortage and the estimated cost of training the future surgical workforce. Ann Surg 250(4):590–597. https://doi.org/10.1097/SLA.0b013e3181b6c90b
Bogen EM, Schlachta CM, Ponsky T (2019) White paper: technology for surgical telementoring-SAGES Project 6 Technology Working Group. Surg Endosc 33(3):684–690. https://doi.org/10.1007/s00464-018-06631-8
Augestad KM, Han H, Paige J, Ponsky T, Schlachta CM, Dunkin B et al (2017) Educational implications for surgical telementoring: a current review with recommendations for future practice, policy, and research. Surg Endosc 31(10):3836–3846. https://doi.org/10.1007/s00464-017-5690-y
Butt K, Augestad KM (2021) Educational value of surgical telementoring. J Surg Oncol 124:231–240
Wyles SM, Miskovic D, Ni Z, Darzi AW, Valori RM, Coleman MG et al (2016) Development and implementation of the Structured Training Trainer Assessment Report (STTAR) in the English National Training Programme for laparoscopic colorectal surgery. Surg Endosc 30(3):993–1003. https://doi.org/10.1007/s00464-015-4281-z
Rosser JC Jr, Bell RL, Harnett B, Rodas E, Murayama M, Merrell R (1999) Use of mobile low-bandwith telemedical techniques for extreme telemedicine applications. J Am Coll Surg 189(4):397–404. https://doi.org/10.1016/s1072-7515(99)00185-4
Goel SS, Greenbaum AB, Patel A, Little SH, Parikh R, Wyler von Ballmoos MC et al (2020) Role of teleproctoring in challenging and innovative structural interventions amid the COVID-19 pandemic and beyond. JACC Cardiovasc Interv 13(16):1945–1948. https://doi.org/10.1016/j.jcin.2020.04.013
Erridge S, Yeung DKT, Patel HRH, Purkayastha S (2019) Telementoring of surgeons: a systematic review. Surg Innov 26(1):95–111. https://doi.org/10.1177/1553350618813250
Huang EY, Knight S, Guetter CR, Davis CH, Moller M, Slama E et al (2019) Telemedicine and telementoring in the surgical specialties: a narrative review. Am J Surg 218(4):760–766. https://doi.org/10.1016/j.amjsurg.2019.07.018
El-Sabawi B, Magee W (2016) The evolution of surgical telementoring: current applications and future directions. Ann Transl Med 4(20):391. https://doi.org/10.21037/atm.2016.10.04
Hung AJ, Chen J, Shah A, Gill IS (2018) Telementoring and telesurgery for minimally invasive procedures. J Urol 199(2):355–369. https://doi.org/10.1016/j.juro.2017.06.082
Shin DH, Dalag L, Azhar RA, Santomauro M, Satkunasivam R, Metcalfe C et al (2015) A novel interface for the telementoring of robotic surgery. BJU Int 116(2):302–308. https://doi.org/10.1111/bju.12985
Shahzad N, Chawla T, Gala T (2019) Telesurgery prospects in delivering healthcare in remote areas. J Pak Med Assoc 69(Supp 1):S69–S71
Marescaux J (2002) Code name: “Lindbergh operation.” Ann Chir 127(1):2–4. https://doi.org/10.1016/s0003-3944(01)00658-7
Xu S, Perez M, Yang K, Perrenot C, Felblinger J, Hubert J (2014) Determination of the latency effects on surgical performance and the acceptable latency levels in telesurgery using the dV-Trainer(®) simulator. Surg Endosc 28(9):2569–2576. https://doi.org/10.1007/s00464-014-3504-z
Anvari M, McKinley C, Stein H (2005) Establishment of the world’s first telerobotic remote surgical service: for provision of advanced laparoscopic surgery in a rural community. Ann Surg 241(3):460–464. https://doi.org/10.1097/01.sla.0000154456.69815.ee
Wirz R, Torres LG, Swaney PJ, Gilbert H, Alterovitz R, Webster RJ 3rd et al (2015) An experimental feasibility study on robotic endonasal telesurgery. Neurosurgery 76(4):479–484 (discussion 484). https://doi.org/10.1227/NEU.0000000000000623
Acemoglu A, Peretti G, Trimarchi M, Hysenbelli J, Krieglstein J, Geraldes A et al (2020) Operating from a distance: robotic vocal cord 5G telesurgery on a cadaver. Ann Intern Med 173(11):940–941. https://doi.org/10.7326/M20-0418
Peters BS, Armijo PR, Krause C, Choudhury SA, Oleynikov D (2018) Review of emerging surgical robotic technology. Surg Endosc 32(4):1636–1655. https://doi.org/10.1007/s00464-018-6079-2
Sheetz KH, Claflin J, Dimick JB (2020) Trends in the adoption of robotic surgery for common surgical procedures. JAMA Netw Open 3(1):e1918911. https://doi.org/10.1001/jamanetworkopen.2019.18911
Khandalavala K, Shimon T, Flores L, Armijo PR, Oleynikov D (2020) Emerging surgical robotic technology: a progression toward microbots. Ann Laparosc Endosc Surg. 5:3. https://doi.org/10.21037/ales.2019.10.02
Abiri A, Juo Y-Y, Tao A, Askari SJ, Pensa J, Bisley JW et al (2019) Artificial palpation in robotic surgery using haptic feedback. Surg Endosc 33(4):1252–1259. https://doi.org/10.1007/s00464-018-6405-8
Shademan A, Decker RS, Opfermann JD, Leonard S, Krieger A, Kim PCW (2016) Supervised autonomous robotic soft tissue surgery. Sci Transl Med 8(337):337ra64. https://doi.org/10.1126/scitranslmed.aad9398
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Yang, Schlachta, Rothenberg, and Reed have no disclosures. Green reports honoraria from Intuitive Surgical for educational events. Hazey reports CME support from Memorial Hospital of Union County, and a patent for a endoluminal gastric restriction device. Madani reports consulting fees from Activ Surgical, and that he is chair of the board for the Global Surgical AI Collaborative. Ponsky reports honoraria and support for travel from MSKSCC and Standford for grand rounds presentations. He received travel support for the AIMED Global Summit 2023. Ali reports participating in the advisory boards and owning stock options in Orchestra Health, OptiSurg, and ClearCam. He has received honoraria and support for travel from AcuityMD. He has received consulting fees from MedTrak, Pristine Surgical, AMBU, and Moon Surgical. Oleynikov reports an NIH grant, honoraria from Medtronic, and stock in Virtual Incision Corp. Szoka reports a research grant from Digbi Health and consulting fees from CSATS for surgical video review services. She is a founder of Endolumik, Inc, in which she owns stock. She holds several patents and is in a licensing agreement with West Virginia University regarding one such patent.
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The SAGES Digital Surgery Working Group., Ali, J.T., Yang, G. et al. Defining digital surgery: a SAGES white paper. Surg Endosc 38, 475–487 (2024). https://doi.org/10.1007/s00464-023-10551-7
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DOI: https://doi.org/10.1007/s00464-023-10551-7