REFERENCES
1. Pfister M, Probst P, Müller PC, et al. Minimally invasive versus open pancreatic surgery: meta-analysis of randomized clinical trials. BJS Open. 2023;7:zrad087.
2. Wellner UF, Küsters S, Sick O, et al. Hybrid laparoscopic versus open pylorus-preserving pancreatoduodenectomy: retrospective matched case comparison in 80 patients. Langenbecks Arch Surg. 2014;399:849-56.
3. Mendoza AS 3rd, Han HS, Yoon YS, Cho JY, Choi Y. Laparoscopy-assisted pancreaticoduodenectomy as minimally invasive surgery for periampullary tumors: a comparison of short-term clinical outcomes of laparoscopy-assisted pancreaticoduodenectomy and open pancreaticoduodenectomy. J Hepatobiliary Pancreat Sci. 2015;22:819-24.
4. Ramshorst TME, van Hilst J, Bannone E, et al; 2022 first Internationally validated European Guidelines Meeting on Minimally Invasive Pancreatic Surgery and the IHPBA Innovation and Research Committees. International survey on opinions and use of robot-assisted and laparoscopic minimally invasive pancreatic surgery: 5-year follow up. HPB. 2024;26:63-72.
5. der Heijde N, Vissers FL, Manzoni A, et al; European Consortium on Minimally Invasive Pancreatic Surgery. Use and outcome of minimally invasive pancreatic surgery in the European E-MIPS registry. HPB. 2023;25:400-8.
6. Gagner M, Pomp A. Laparoscopic pylorus-preserving pancreatoduodenectomy. Surg Endosc. 1994;8:408-10.
7. Palanivelu C, Senthilnathan P, Sabnis SC, et al. Randomized clinical trial of laparoscopic versus open pancreatoduodenectomy for periampullary tumours. Br J Surg. 2017;104:1443-50.
8. Poves I, Burdío F, Morató O, et al. Comparison of perioperative outcomes between laparoscopic and open approach for pancreatoduodenectomy: the PADULAP randomized controlled trial. Ann Surg. 2018;268:731-9.
9. Wang M, Pan S, Qin T, et al. Short-term outcomes following laparoscopic vs open pancreaticoduodenectomy in patients with pancreatic ductal adenocarcinoma: a randomized clinical trial. JAMA Surg. 2023;158:1245-53.
10. Baker EH, Ross SW, Seshadri R, et al. Robotic pancreaticoduodenectomy for pancreatic adenocarcinoma: role in 2014 and beyond. J Gastrointest Oncol. 2015;6:396-405.
11. Shyr YM, Wang SE, Chen SC, Shyr BU, Shyr BS. Robotic pancreaticoduodenectomy for pancreatic head cancer and periampullary lesions. Ann Gastroenterol Surg. 2021;5:589-96.
12. Nickel F, Wise PA, Müller PC, et al. Short-term outcomes of robotic versus open pancreatoduodenectomy: propensity score-matched analysis. Ann Surg. 2024;279:665-70.
13. Müller PC, Breuer E, Nickel F, et al. Robotic distal pancreatectomy: a novel standard of care? Ann Surg. 2023;278:253-9.
14. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2:719-31.
15. McCarthy MLM, Nathaniel Rochester, Claude E. A proposal for the dartmouth summer research project on artificial intelligence. AI Magazine. 1995;27:4.
16. De Simone B, Chouillard E, Gumbs AA, Loftus TJ, Kaafarani H, Catena F. Artificial intelligence in surgery: the emergency surgeon's perspective (the ARIES project). Discov Health Syst. 2022;1:9.
17. Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg. 2022;276:363-9.
18. Sadda P, Imamoglu M, Dombrowski M, Papademetris X, Bahtiyar MO, Onofrey J. Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery. Int J Comput Assist Radiol Surg. 2019;14:227-35.
19. Mascagni P, Vardazaryan A, Alapatt D, et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg. 2022;275:955-61.
20. Kitaguchi D, Takeshita N, Matsuzaki H, et al. Computer-assisted real-time automatic prostate segmentation during TaTME: a single-center feasibility study. Surg Endosc. 2021;35:2493-9.
21. Kuemmerli C, Rössler F, Berchtold C, et al. Artificial intelligence in pancreatic surgery: current applications. Journal of Pancreatology. 2023;6:74-81.
22. Asbun HJ, Moekotte AL, Vissers FL, et al; International Study Group on Minimally Invasive Pancreas Surgery (I-MIPS). The miami international evidence-based guidelines on minimally invasive pancreas resection. Ann Surg. 2020;271:1-14.
23. Yan Y, Hua Y, Chang C, Zhu X, Sha Y, Wang B. Laparoscopic versus open pancreaticoduodenectomy for pancreatic and periampullary tumor: a meta-analysis of randomized controlled trials and non-randomized comparative studies. Front Oncol. 2022;12:1093395.
24. Abu Hilal M, van Ramshorst TME, Boggi U, et al; Collaborators. The Brescia internationally validated European guidelines on minimally invasive pancreatic surgery (EGUMIPS). Ann Surg. 2024;279:45-57.
25. de Rooij T, van Hilst J, van Santvoort H, et al; Dutch Pancreatic Cancer Group. Minimally invasive versus open distal pancreatectomy (LEOPARD): a multicenter patient-blinded randomized controlled trial. Ann Surg. 2019;269:2-9.
26. Korrel M, Jones LR, van Hilst J, et al; European Consortium on Minimally Invasive Pancreatic Surgery (E-MIPS). Minimally invasive versus open distal pancreatectomy for resectable pancreatic cancer (DIPLOMA): an international randomised non-inferiority trial. Lancet Reg Health Eur. 2023;31:100673.
27. Yin T, Qin T, Wei K, et al. Comparison of safety and effectiveness between laparoscopic and open pancreatoduodenectomy: a systematic review and meta-analysis. Int J Surg. 2022;105:106799.
28. Nickel F, Haney CM, Kowalewski KF, et al. Laparoscopic versus open pancreaticoduodenectomy: a systematic review and meta-analysis of randomized controlled trials. Ann Surg. 2020;271:54-66.
29. van Hilst J, de Rooij T, Bosscha K, et al; Dutch Pancreatic Cancer Group. Laparoscopic versus open pancreatoduodenectomy for pancreatic or periampullary tumours (LEOPARD-2): a multicentre, patient-blinded, randomised controlled phase 2/3 trial. Lancet Gastroenterol Hepatol. 2019;4:199-207.
30. Choi M, Hwang HK, Rho SY, Lee WJ, Kang CM. Comparing laparoscopic and open pancreaticoduodenectomy in patients with pancreatic head cancer: oncologic outcomes and inflammatory scores. J Hepatobiliary Pancreat Sci. 2020;27:124-31.
31. Sharpe SM, Talamonti MS, Wang CE, et al. Early national experience with laparoscopic pancreaticoduodenectomy for ductal adenocarcinoma: a comparison of laparoscopic pancreaticoduodenectomy and open pancreaticoduodenectomy from the national cancer data base. J Am Coll Surg. 2015;221:175-84.
32. Yan JF, Pan Y, Chen K, Zhu HP, Chen QL. Minimally invasive pancreatoduodenectomy is associated with lower morbidity compared to open pancreatoduodenectomy: an updated meta-analysis of randomized controlled trials and high-quality nonrandomized studies. Medicine. 2019;98:e16730.
33. Giulianotti PC, Coratti A, Angelini M, et al. Robotics in general surgery: personal experience in a large community hospital. Arch Surg. 2003;138:777-84.
34. van Ramshorst TME, van Bodegraven EA, Zampedri P, Kasai M, Besselink MG, Abu Hilal M. Robot-assisted versus laparoscopic distal pancreatectomy: a systematic review and meta-analysis including patient subgroups. Surg Endosc. 2023;37:4131-43.
35. Liu Q, Zhao Z, Zhang X, et al. Perioperative and oncological outcomes of robotic versus open pancreaticoduodenectomy in low-risk surgical candidates: a multicenter propensity score-matched study. Ann Surg. 2023;277:e864-71.
36. Tao HS, Lin JY, Luo W, et al. Application of real-time augmented reality laparoscopic navigation in splenectomy for massive splenomegaly. World J Surg. 2021;45:2108-15.
37. Müller PC, Haslebacher C, Steinemann DC, et al. Image-guided minimally invasive endopancreatic surgery using a computer-assisted navigation system. Surg Endosc. 2021;35:1610-7.
38. Müller PC, Steinemann DC, Nickel F, et al. Transduodenal-transpapillary endopancreatic surgery with a rigid resectoscope: experiments on ex vivo, in vivo animal models and human cadavers. Surg Endosc. 2017;31:4131-5.
39. Miyamoto R, Oshiro Y, Nakayama K, et al. Three-dimensional simulation of pancreatic surgery showing the size and location of the main pancreatic duct. Surg Today. 2017;47:357-64.
40. Lin C, Gao J, Zheng H, et al. Three-dimensional visualization technology used in pancreatic surgery: a valuable tool for surgical trainees. J Gastrointest Surg. 2020;24:866-73.
41. Fang CH, Zhu W, Wang H, et al. A new approach for evaluating the resectability of pancreatic and periampullary neoplasms. Pancreatology. 2012;12:364-71.
42. Fang CH, Kong D, Wang X, et al. Three-dimensional reconstruction of the peripancreatic vascular system based on computed tomographic angiography images and its clinical application in the surgical management of pancreatic tumors. Pancreas. 2014;43:389-95.
43. Bari H, Wadhwani S, Dasari BVM. Role of artificial intelligence in hepatobiliary and pancreatic surgery. World J Gastrointest Surg. 2021;13:7-18.
44. Onda S, Okamoto T, Kanehira M, et al. Identification of inferior pancreaticoduodenal artery during pancreaticoduodenectomy using augmented reality-based navigation system. J Hepatobiliary Pancreat Sci. 2014;21:281-7.
45. Tang R, Yang W, Hou Y, et al. Augmented reality-assisted pancreaticoduodenectomy with superior mesenteric vein resection and reconstruction. Gastroenterol Res Pract. 2021;2021:9621323.
46. Mieog JSD, Achterberg FB, Zlitni A, et al. Fundamentals and developments in fluorescence-guided cancer surgery. Nat Rev Clin Oncol. 2022;19:9-22.
47. Metildi CA, Kaushal S, Pu M, et al. Fluorescence-guided surgery with a fluorophore-conjugated antibody to carcinoembryonic antigen (CEA), that highlights the tumor, improves surgical resection and increases survival in orthotopic mouse models of human pancreatic cancer. Ann Surg Oncol. 2014;21:1405-11.
48. Juhl K, Christensen A, Rubek N, Karnov KKS, von Buchwald C, Kjaer A. Improved surgical resection of metastatic pancreatic cancer using uPAR targeted in vivo fluorescent guidance: comparison with traditional white light surgery. Oncotarget. 2019;10:6308-16.
49. Oba A, Inoue Y, Sato T, et al. Impact of indocyanine green-fluorescence imaging on distal pancreatectomy with celiac axis resection combined with reconstruction of the left gastric artery. HPB. 2019;21:619-25.
50. Wagner M, Brandenburg JM, Bodenstedt S, et al. Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data. Surg Endosc. 2022;36:8568-91.
51. Garrow CR, Kowalewski KF, Li L, et al. Machine learning for surgical phase recognition: a systematic review. Ann Surg. 2021;273:684-93.
52. Kitaguchi D, Takeshita N, Matsuzaki H, et al. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc. 2020;34:4924-31.
53. Kitaguchi D, Takeshita N, Matsuzaki H, et al. Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision. Surg Endosc. 2022;36:1143-51.
54. Ramesh S, Dall’Alba D, Gonzalez C, et al. Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures. Int J Comput Assist Radiol Surg. 2021;16:1111-9.
55. Ward TM, Hashimoto DA, Ban Y, et al. Automated operative phase identification in peroral endoscopic myotomy. Surg Endosc. 2021;35:4008-15.
56. Kiyasseh D, Ma R, Haque TF, et al. A vision transformer for decoding surgeon activity from surgical videos. Nat Biomed Eng. 2023;7:780-96.
57. Fan B, Li HX, Hu Y. An intelligent decision system for intraoperative somatosensory evoked potential monitoring. IEEE Trans Neural Syst Rehabil Eng. 2016;24:300-7.
58. Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020;14:369-81.
59. Smits FJ, Henry AC, Besselink MG, et al; Dutch Pancreatic Cancer Group. Algorithm-based care versus usual care for the early recognition and management of complications after pancreatic resection in the Netherlands: an open-label, nationwide, stepped-wedge cluster-randomised trial. Lancet. 2022;399:1867-75.
60. Merath K, Hyer JM, Mehta R, et al. Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery. J Gastrointest Surg. 2020;24:1843-51.
61. Sahara K, Paredes AZ, Tsilimigras DI, et al. Machine learning predicts unpredicted deaths with high accuracy following hepatopancreatic surgery. Hepatobiliary Surg Nutr. 2021;10:20-30.
62. Kambakamba P, Mannil M, Herrera PE, et al. The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: a proof-of-principle study. Surgery. 2020;167:448-54.
63. Skawran SM, Kambakamba P, Baessler B, et al. Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula? Eur J Radiol. 2021;140:109733.
64. Verma A, Balian J, Hadaya J, et al. Machine learning-based prediction of postoperative pancreatic fistula following pancreaticoduodenectomy. Ann Surg. 2024;280:325-31.
65. Ashraf Ganjouei A, Romero-Hernandez F, Wang JJ, et al. A machine learning approach to predict postoperative pancreatic fistula after pancreaticoduodenectomy using only preoperatively known data. Ann Surg Oncol. 2023;30:7738-47.
66. Haney CM, Karadza E, Limen EF, et al. Training and learning curves in minimally invasive pancreatic surgery: from simulation to mastery. Journal of Pancreatology. 2020;3:101-10.
67. Müller PC, Kuemmerli C, Cizmic A, et al. Learning curves in open, laparoscopic, and robotic pancreatic surgery: a systematic review and proposal of a standardization. Ann Surg Open. 2022;3:e111.
68. Müller PC, Müller-Stich BP, Hackert T, Nickel F. Robotic pancreaticoduodenectomy after the learning curve-a new hope. Hepatobiliary Surg Nutr. 2022;11:489-91.
69. Rice MK, Hodges JC, Bellon J, et al. Association of mentorship and a formal robotic proficiency skills curriculum with subsequent generations’ learning curve and safety for robotic pancreaticoduodenectomy. JAMA Surg. 2020;155:607-15.
70. Zwart MJW, Nota CLM, de Rooij T, et al; Dutch Pancreatic Cancer Group. Outcomes of a multicenter training program in robotic pancreatoduodenectomy (LAELAPS-3). Ann Surg. 2022;276:e886-95.
71. Fazlollahi AM, Bakhaidar M, Alsayegh A, et al. Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial. JAMA Netw Open. 2022;5:e2149008.
72. Kiyasseh D, Laca J, Haque TF, et al. A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons. Commun Med. 2023;3:42.
73. Bissonnette V, Mirchi N, Ledwos N, Alsidieri G, Winkler-Schwartz A, Del Maestro RF; Neurosurgical Simulation & Artificial Intelligence Learning Centre. Artificial intelligence distinguishes surgical training levels in a virtual reality spinal task. J Bone Joint Surg Am. 2019;101:e127.
74. Satapathy P, Hermis AH, Rustagi S, Pradhan KB, Padhi BK, Sah R. Artificial intelligence in surgical education and training: opportunities, challenges, and ethical considerations - correspondence. Int J Surg. 2023;109:1543-4.
75. Kowalewski KF, Hendrie JD, Schmidt MW, et al. Development and validation of a sensor- and expert model-based training system for laparoscopic surgery: the iSurgeon. Surg Endosc. 2017;31:2155-65.
76. Nickel F, Cizmic A, Chand M. Telestration and augmented reality in minimally invasive surgery: an invaluable tool in the age of COVID-19 for remote proctoring and telementoring. JAMA Surg. 2022;157:169-70.
77. Pedrett R, Mascagni P, Beldi G, Padoy N, Lavanchy JL. Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review. Surg Endosc. 2023;37:7412-24.
78. Arora A, Alderman JE, Palmer J, et al. The value of standards for health datasets in artificial intelligence-based applications. Nat Med. 2023;29:2929-38.
79. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.
80. Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJY, Kann BH. Randomized clinical trials of machine learning interventions in health care: a systematic review. JAMA Netw Open. 2022;5:e2233946.
81. Hasan HE, Jaber D, Khabour OF, Alzoubi KH. Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study. BMC Med Ethics. 2024;25:55.
