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Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023; 73:17-48.","DOI":"10.3322\/caac.21763"},{"key":"2","unstructured":"2. European Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol. 2018; 69:182-236."},{"key":"3","doi-asserted-by":"crossref","unstructured":"3. Vogel A, Martinelli E, ESMO Guidelines Committee. Updated treatment recommendations for hepatocellular carcinoma (HCC) from the ESMO Clinical Practice Guidelines. Ann Oncol. 2021; 32:801-805.","DOI":"10.1016\/j.annonc.2021.02.014"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Xie D, Shi J, Zhou J, Fan J, Gao Q. Clinical practice guidelines and real-life practice in hepatocellular carcinoma: A Chinese perspective. Clin Mol Hepatol. 2023; 29:206-216.","DOI":"10.3350\/cmh.2022.0402"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Kubo S, Shinkawa H, Asaoka Y, <i>et al.<\/i> Liver Cancer Study Group of Japan Clinical Practice Guidelines for Intrahepatic Cholangiocarcinoma. Liver Cancer. 2022; 11:290-314.","DOI":"10.1159\/000522403"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Di Benedetto F, Magistri P, Di Sandro S, <i>et al.<\/i> Safety and Efficacy of Robotic vs Open Liver Resection for Hepatocellular Carcinoma. JAMA Surg. 2023; 158:46-54.","DOI":"10.1001\/jamasurg.2022.5697"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Wang H, Fu T, Du Y, <i>et al.<\/i> Scientific discovery in the age of artificial intelligence. Nature. 2023; 620:47-60.","DOI":"10.1038\/s41586-023-06221-2"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022; 23:40-55.","DOI":"10.1038\/s41580-021-00407-0"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Dong H, Zhang Y, Gu H, Konz N, Zhang Y, Mazurowski MA. SWSSL: Sliding window-based self-supervised learning for anomaly detection in high-resolution images. IEEE Trans Med Imaging. 2023; 42:3860-3870.","DOI":"10.1109\/TMI.2023.3314318"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Tu Z, Huang Z, Chen Y, Kang D, Bao L, Yang B, Yuan J. Consistent 3D hand reconstruction in video <i>via<\/i> self-supervised learning. IEEE Trans Pattern Anal Mach Intell. 2023; 45:9469-9485.","DOI":"10.1109\/TPAMI.2023.3247907"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Br\u00f6ker F, Holt LL, Roads BD, Dayan P, Love BC. Demystifying unsupervised learning: how it helps and hurts. Trends Cogn Sci. 2024; 28:974-986.","DOI":"10.1016\/j.tics.2024.09.005"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12. Baur C, Denner S, Wiestler B, Navab N, Albarqouni S. Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study. Med Image Anal. 2021; 69:101952.","DOI":"10.1016\/j.media.2020.101952"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018; 284:603-619.","DOI":"10.1111\/joim.12822"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, S\u00e1nchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017; 42:60-88.","DOI":"10.1016\/j.media.2017.07.005"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521:436-444.","DOI":"10.1038\/nature14539"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Huang J, Wang M, Ju H, Shi Z, Ding W, Zhang D. SD-CNN: A static-dynamic convolutional neural network for functional brain networks. Med Image Anal. 2023; 83:102679.","DOI":"10.1016\/j.media.2022.102679"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Zeng Q, Klein C, Caruso S, <i>et al.<\/i> Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J Hepatol. 2022; 77:116-127.","DOI":"10.1016\/j.jhep.2022.01.018"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Moustris GP, Hiridis SC, Deliparaschos KM, Konstantinidis KM. Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature. Int J Med Robot. 2011; 7:375-392.","DOI":"10.1002\/rcs.408"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Di Saverio S, Catena F. Images in clinical medicine: Rotation of the liver in pneumoperitoneum. N Engl J Med. 2014; 371:e29.","DOI":"10.1056\/NEJMicm1400032"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20. Aron M, Gill IS. Robotic radical cystectomy: so far, so good--what next? Eur Urol. 2015; 67:361-362.","DOI":"10.1016\/j.eururo.2014.12.054"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21. Liu Z, Li X, Tian S, Zhu T, Yao Y, Tao Y. Superiority of robotic surgery for cervical cancer in comparison with traditional approaches: A systematic review and meta-analysis. Int J Surg. 2017; 40:145-154.","DOI":"10.1016\/j.ijsu.2017.02.062"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Tao XM. Virtual and augmented reality enhanced by touch. Nature. 2019; 575:453-454.","DOI":"10.1038\/d41586-019-03506-3"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23. Mahtab EAF, Egorova AD. Current and future applications of virtual reality technology for cardiac interventions. Nat Rev Cardiol. 2022; 19:779-780.","DOI":"10.1038\/s41569-022-00789-4"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Bruno RR, Wolff G, Wernly B, Masyuk M, Piayda K, Leaver S, Erkens R, Oehler D, Afzal S, Heidari H, Kelm M, Jung C. Virtual and augmented reality in critical care medicine: the patient&apos;s, clinician&apos;s, and researcher&apos;s perspective. Crit Care. 2022; 26:326.","DOI":"10.1186\/s13054-022-04202-x"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Jin C, Dai L, Wang T. The application of virtual reality in the training of laparoscopic surgery: A systematic review and meta-analysis. Int J Surg. 2021; 87:105859.","DOI":"10.1016\/j.ijsu.2020.11.022"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. May M. How virtual reality therapy is shaping mental health. Nat Med. 2024; 30:1797-1799.","DOI":"10.1038\/d41591-024-00032-2"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Saillard C, Schmauch B, Laifa O, <i>et al.<\/i> Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology. 2020; 72:2000-2013.","DOI":"10.1002\/hep.31207"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. Koga H, Iwamoto H, Suzuki H, Shimose S, Nakano M, Kawaguchi T. Clinical practice guidelines and real-life practice in hepatocellular carcinoma: A Japanese perspective. Clin Mol Hepatol. 2023; 29:242-251.","DOI":"10.3350\/cmh.2023.0102"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29. Korean Liver Cancer Association (KLCA), National Cancer Center (NCC) Korea. 2022 KLCA-NCC Korea practice guidelines for the management of hepatocellular carcinoma. Clin Mol Hepatol. 2022; 28:583-705.","DOI":"10.3350\/cmh.2022.0294"},{"key":"30","doi-asserted-by":"crossref","unstructured":"30. Suddle A, Reeves H, Hubner R, Marshall A, Rowe I, Tiniakos D, Hubscher S, Callaway M, Sharma D, See TC, Hawkins M, Ford-Dunn S, Selemani S, Meyer T. British Society of Gastroenterology guidelines for the management of hepatocellular carcinoma in adults. Gut. 2024; 73:1235-1268.","DOI":"10.1136\/gutjnl-2023-331695"},{"key":"31","doi-asserted-by":"crossref","unstructured":"31. Jonas S, Bechstein WO, Steinm\u00fcller T, Herrmann M, Radke C, Berg T, Settmacher U, Neuhaus P. Vascular invasion and histopathologic grading determine outcome after liver transplantation for hepatocellular carcinoma in cirrhosis. Hepatology. 2001; 33:1080-1086.","DOI":"10.1053\/jhep.2001.23561"},{"key":"32","doi-asserted-by":"crossref","unstructured":"32. Cucchetti A, Piscaglia F, Grigioni AD, Ravaioli M, Cescon M, Zanello M, Grazi GL, Golfieri R, Grigioni WF, Pinna AD. Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study. J Hepatol. 2010; 52:880-888.","DOI":"10.1016\/j.jhep.2009.12.037"},{"key":"33","doi-asserted-by":"crossref","unstructured":"33. Xia TY, Zhou ZH, Meng XP, Zha JH, Yu Q, Wang WL, Song Y, Wang YC, Tang TY, Xu J, Zhang T, Long XY, Liang Y, Xiao WB, Ju SH. Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model. Radiology. 2023; 307:e222729.","DOI":"10.1148\/radiol.222729"},{"key":"34","doi-asserted-by":"crossref","unstructured":"34. Mazzaferro V, Llovet JM, Miceli R, <i>et al.<\/i> Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. Lancet Oncol. 2009; 10:35-43.","DOI":"10.1016\/S0739-5930(09)79300-6"},{"key":"35","doi-asserted-by":"crossref","unstructured":"35. Jin Y, Li W, Wu Y, Wang Q, Xiang Z, Long Z, Liang H, Zou J, Zhu Z, Dai X. Online interpretable dynamic prediction models for clinically significant posthepatectomy liver failure based on machine learning algorithms: a retrospective cohort study. Int J Surg. 2024; 110:7047-7057.","DOI":"10.1097\/JS9.0000000000001764"},{"key":"36","doi-asserted-by":"crossref","unstructured":"36. Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges. Theranostics. 2019; 9:1303-1322.","DOI":"10.7150\/thno.30309"},{"key":"37","doi-asserted-by":"crossref","unstructured":"37. Oh N, Kim JH, Rhu J, Jeong WK, Choi GS, Kim J, Joh JW. Comprehensive deep learning-based assessment of living liver donor CT angiography: From vascular segmentation to volumetric analysis. Int J Surg. 2024; 110:6551-6557.","DOI":"10.1097\/JS9.0000000000001829"},{"key":"38","doi-asserted-by":"crossref","unstructured":"38. Dai J, Wang H, Xu Y, Chen X, Tian R. Clinical application of AI-based PET images in oncological patients. Semin Cancer Biol. 2023; 91:124-142.","DOI":"10.1016\/j.semcancer.2023.03.005"},{"key":"39","doi-asserted-by":"crossref","unstructured":"39. Keyl J, Hosch R, Berger A, <i>et al.<\/i> Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer. J Cachexia Sarcopenia Muscle. 2023; 14:545-552.","DOI":"10.1002\/jcsm.13158"},{"key":"40","doi-asserted-by":"crossref","unstructured":"40. Birkmeyer JD, Finks JF, O&apos;Reilly A, Oerline M, Carlin AM, Nunn AR, Dimick J, Banerjee M, Birkmeyer NJ; Michigan Bariatric Surgery Collaborative. Surgical skill and complication rates after bariatric surgery. N Engl J Med. 2013; 369:1434-1442.","DOI":"10.1056\/NEJMsa1300625"},{"key":"41","doi-asserted-by":"crossref","unstructured":"41. Koffron A, Geller D, Gamblin TC, Abecassis M. Laparoscopic liver surgery: shifting the management of liver tumors. Hepatology. 2006; 44:1694-1700.","DOI":"10.1002\/hep.21485"},{"key":"42","doi-asserted-by":"crossref","unstructured":"42. Zhang Y, Zou Y, Liu PX. Point cloud registration in laparoscopic liver surgery using keypoint correspondence registration network. IEEE Trans Med Imaging. 2025; 44:749-760.","DOI":"10.1109\/TMI.2024.3457228"},{"key":"43","doi-asserted-by":"crossref","unstructured":"43. Madani A, Namazi B, Altieri MS, Hashimoto DA, Rivera AM, Pucher PH, Navarrete-Welton A, Sankaranarayanan G, Brunt LM, Okrainec A, Alseidi A. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg. 2022; 276:363-369.","DOI":"10.1097\/SLA.0000000000004594"},{"key":"44","doi-asserted-by":"crossref","unstructured":"44. Ramalhinho J, Yoo S, Dowrick T, Koo B, Somasundaram M, Gurusamy K, Hawkes DJ, Davidson B, Blandford A, Clarkson MJ. The value of augmented reality in surgery - a usability study on laparoscopic liver surgery. Med Image Anal. 2023; 90:102943.","DOI":"10.1016\/j.media.2023.102943"},{"key":"45","doi-asserted-by":"crossref","unstructured":"45. Jeong IG, Khandwala YS, Kim JH, Han DH, Li S, Wang Y, Chang SL, Chung BI. Association of robotic-assisted vs laparoscopic radical nephrectomy with perioperative outcomes and health care costs, 2003 to 2015. JAMA. 2017; 318:1561-1568.","DOI":"10.1001\/jama.2017.14586"},{"key":"46","doi-asserted-by":"crossref","unstructured":"46. Feng Q, Yuan W, Li T, Tang B, Jia B, Zhou Y, Zhang W, Zhao R, Zhang C, Cheng L, Zhang X, Liang F, He G, Wei Y, Xu J; REAL Study Group. Robotic versus laparoscopic surgery for middle and low rectal cancer (REAL): short-term outcomes of a multicentre randomised controlled trial. Lancet Gastroenterol Hepatol. 2022; 7:991-1004.","DOI":"10.1016\/S2468-1253(22)00248-5"},{"key":"47","doi-asserted-by":"crossref","unstructured":"47. Birgin E, Heibel M, Hetjens S, Rasbach E, Reissfelder C, T\u00e9oule P, Rahbari NN. Robotic versus laparoscopic hepatectomy for liver malignancies (ROC&apos;N&apos;ROLL): a single-centre, randomised, controlled, single-blinded clinical trial. Lancet Reg Health Eur. 2024; 43:100972.","DOI":"10.1016\/j.lanepe.2024.100972"},{"key":"48","doi-asserted-by":"crossref","unstructured":"48. Lee KW, Choi Y, Hong SK, Lee S, Hong SY, Suh S, Han ES, Yi NJ, Suh KS. Laparoscopic donor and recipient hepatectomy followed by robot-assisted liver graft implantation in living donor liver transplantation. Am J Transplant. 2022; 22:1230-1235.","DOI":"10.1111\/ajt.16943"},{"key":"49","doi-asserted-by":"crossref","unstructured":"49. Broering DC, Raptis DA, Elsheikh Y. Pioneering fully robotic donor hepatectomy and robotic recipient liver graft implantation - a new horizon in liver transplantation. Int J Surg. 2024; 110:1333-1336.","DOI":"10.1097\/JS9.0000000000001031"},{"key":"50","doi-asserted-by":"crossref","unstructured":"50. Weiner SG. Invoking a medical axiom for buprenorphine initiation-see one, do one, teach one. JAMA Netw Open. 2023; 6:e2342980.","DOI":"10.1001\/jamanetworkopen.2023.42980"},{"key":"51","doi-asserted-by":"crossref","unstructured":"51. Celentano V, Smart N, McGrath J, <i>et al.<\/i> LAP-VEGaS practice guidelines for reporting of educational videos in laparoscopic surgery: a joint trainers and trainees consensus statement. Ann Surg. 2018; 268:920-926.","DOI":"10.1097\/SLA.0000000000002725"},{"key":"52","doi-asserted-by":"crossref","unstructured":"52. Jin Y, Dou Q, Chen H, Yu L, Qin J, Fu CW, Heng PA. SV-RCNet: workflow recognition from surgical videos using recurrent convolutional network. IEEE Trans Med Imaging. 2018; 37:1114-1126.","DOI":"10.1109\/TMI.2017.2787657"},{"key":"53","doi-asserted-by":"crossref","unstructured":"53. Garrow CR, Kowalewski KF, Li L, Wagner M, Schmidt MW, Engelhardt S, Hashimoto DA, Kenngott HG, Bodenstedt S, Speidel S, M\u00fcller-Stich BP, Nickel F. Machine learning for surgical phase recognition: a systematic review. Ann Surg. 2021; 273:684-693.","DOI":"10.1097\/SLA.0000000000004425"},{"key":"54","doi-asserted-by":"crossref","unstructured":"54. Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N. EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans Med Imaging. 2017; 36:86-97.","DOI":"10.1109\/TMI.2016.2593957"},{"key":"55","doi-asserted-by":"crossref","unstructured":"55. Kitaguchi D, Takeshita N, Matsuzaki H, Oda T, Watanabe M, Mori K, Kobayashi E, Ito M. Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: experimental research. Int J Surg. 2020; 79:88-94.","DOI":"10.1016\/j.ijsu.2020.05.015"},{"key":"56","doi-asserted-by":"crossref","unstructured":"56. Zappella L, B\u00e9jar B, Hager G, Vidal R. Surgical gesture classification from video and kinematic data. Med Image Anal. 2013; 17:732-745.","DOI":"10.1016\/j.media.2013.04.007"},{"key":"57","doi-asserted-by":"crossref","unstructured":"57. Breda A, Territo A. Virtual reality simulators for robot-assisted surgery. Eur Urol. 2016; 69:1081-1082.","DOI":"10.1016\/j.eururo.2015.11.026"},{"key":"58","doi-asserted-by":"crossref","unstructured":"58. Yoganathan S, Finch DA, Parkin E, Pollard J. 360\u00b0 virtual reality video for the acquisition of knot tying skills: a randomised controlled trial. Int J Surg. 2018; 54:24-27.","DOI":"10.1016\/j.ijsu.2018.04.002"},{"key":"59","doi-asserted-by":"crossref","unstructured":"59. Pryor AD, Lendvay T, Jones A, Ib\u00e1\u00f1ez B, Pugh C. An American Board of Surgery pilot of video assessment of surgeon technical performance in surgery. Ann Surg. 2023; 277:591-595.","DOI":"10.1097\/SLA.0000000000005804"},{"key":"60","doi-asserted-by":"crossref","unstructured":"60. Wu J, Hines OJ. Using artificial intelligence to assess surgeon skill. JAMA Surg. 2023; 158:e231140.","DOI":"10.1001\/jamasurg.2023.1140"},{"key":"61","doi-asserted-by":"crossref","unstructured":"61. Loftus TJ, Ruppert MM, Ozrazgat-Baslanti T, Balch JA, Efron PA, Tighe PJ, Hogan WR, Rashidi P, Upchurch GR Jr, Bihorac A. Association of postoperative undertriage to hospital wards with mortality and morbidity. JAMA Netw Open. 2021; 4:e2131669.","DOI":"10.1001\/jamanetworkopen.2021.31669"},{"key":"62","doi-asserted-by":"crossref","unstructured":"62. Ren Y, Loftus TJ, Datta S, Ruppert MM, Guan Z, Miao S, Shickel B, Feng Z, Giordano C, Upchurch GR Jr, Rashidi P, Ozrazgat-Baslanti T, Bihorac A. Performance of a machine learning algorithm using electronic health record data to predict postoperative complications and report on a mobile platform. JAMA Netw Open. 2022; 5:e2211973.","DOI":"10.1001\/jamanetworkopen.2022.11973"},{"key":"63","doi-asserted-by":"crossref","unstructured":"63. Bonde A, Varadarajan KM, Bonde N, Troelsen A, Muratoglu OK, Malchau H, Yang AD, Alam H, Sillesen M. Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study. Lancet Digit Health. 2021; 3:e471-e485.","DOI":"10.1016\/S2589-7500(21)00084-4"},{"key":"64","doi-asserted-by":"crossref","unstructured":"64. Oikonomou EK, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. Eur Heart J. 2024; 45:3204-3218.","DOI":"10.1093\/eurheartj\/ehae415"},{"key":"65","doi-asserted-by":"crossref","unstructured":"65. Lee N. Robotic surgery: where are we now? Lancet. 2014; 384:1417.","DOI":"10.1016\/S0140-6736(14)61851-1"},{"key":"66","unstructured":"66. Joshi RM, Telang B, Soni G, Khalife A.. Overview of perspectives on cancer, newer therapies, and future directions. Oncol Transl Med. 2024; 10:105-109."},{"key":"67","doi-asserted-by":"crossref","unstructured":"67. Saeidi H, Opfermann JD, Kam M, Wei S, Leonard S, Hsieh MH, Kang JU, Krieger A. Autonomous robotic laparoscopic surgery for intestinal anastomosis. Sci Robot. 2022; 7:eabj2908.","DOI":"10.1126\/scirobotics.abj2908"},{"key":"68","doi-asserted-by":"crossref","unstructured":"68. Horesh D, Kohavi S, Shilony-Nalaboff L, Rudich N, Greenman D, Feuerstein JS, Abbasi MR. Virtual reality combined with artificial intelligence (VR-AI) reduces hot flashes and improves psychological well-being in women with breast and ovarian cancer: a pilot study. Healthcare (Basel). 2022; 10:2261.","DOI":"10.3390\/healthcare10112261"},{"key":"69","doi-asserted-by":"crossref","unstructured":"69. Gao Y, Kruger U, Intes X, Schwaitzberg S, De S. A machine learning approach to predict surgical learning curves. Surgery. 2020; 167:321-327.","DOI":"10.1016\/j.surg.2019.10.008"},{"key":"70","doi-asserted-by":"crossref","unstructured":"70. Moglia A, Georgiou K, Georgiou E, Satava RM, Cuschieri A. 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