REFERENCES
2. Gorji L, Brown ZJ, Limkemann A, Schenk AD, Pawlik TM. Liver transplant as a treatment of primary and secondary liver neoplasms. JAMA Surg 2024;159:211-8.
3. Sucher R, Sucher E. Artificial intelligence is poised to revolutionize human liver allocation and decrease medical costs associated with liver transplantation. Hepatobiliary Surg Nutr 2020;9:679-81.
4. Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol 2023;78:1216-33.
5. Balsano C, Burra P, Duvoux C, Alisi A, Piscaglia F, Gerussi A. Special Interest Group (SIG) Artificial Intelligence and Liver Disease; Italian Association for the Study of Liver (AISF). Artificial Intelligence and liver: opportunities and barriers. Dig Liver Dis 2023;55:1455-61.
6. Ge J, Kim WR, Lai JC, Kwong AJ. “Beyond MELD” - Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation. J Hepatol 2022;76:1318-29.
7. Briceño J. Artificial intelligence and organ transplantation: challenges and expectations. Curr Opin Organ Transplant 2020;25:393-8.
8. Kahn J, Wagner D, Homfeld N, Müller H, Kniepeiss D, Schemmer P. Both sarcopenia and frailty determine suitability of patients for liver transplantation - a systematic review and meta-analysis of the literature. Clin Transplant 2018;32:e13226.
9. Angeli P, Garcia-Tsao G, Nadim MK, Parikh CR. News in pathophysiology, definition and classification of hepatorenal syndrome: a step beyond the International Club of Ascites (ICA) consensus document. J Hepatol 2019;71:811-22.
10. Hu C, Anjur V, Saboo K, et al. Low predictability of readmissions and death using machine learning in cirrhosis. Am J Gastroenterol 2021;116:336-46.
11. Liu Z, Xu J, Que S, et al. Recent progress and future direction for the application of multiomics data in clinical liver transplantation. J Clin Transl Hepatol 2022;10:363-73.
12. Martorell-Marugán J, Tabik S, Benhammou Y, et al. Deep learning in omics data analysis and precision medicine. In: Husi H, editor. Computational biology [Internet]. Brisbane (AU): Codon Publications; 2019. Chapter 3.
13. D’Adamo GL, Widdop JT, Giles EM. The future is now? Clinical and translational aspects of “Omics” technologies. Immunol Cell Biol 2021;99:168-76.
14. Ba R, Geffard E, Douillard V, et al. Surfing the big data wave: omics data challenges in transplantation. Transplantation 2022;106:e114-25.
15. Lai JC, Sonnenday CJ, Tapper EB, et al. Frailty in liver transplantation: an expert opinion statement from the American society of transplantation liver and intestinal community of practice. Am J Transplant 2019;19:1896-906.
16. Tandon P, Montano-Loza AJ, Lai JC, Dasarathy S, Merli M. Sarcopenia and frailty in decompensated cirrhosis. J Hepatol 2021;75:S147-62.
17. Maurice JB, Nwaogu A, Gouda M, Shaw O, Sanchez-Fueyo A, Zen Y. Acute Antibody-mediated rejection in liver transplantation: impact and applicability of the Banff working group on liver allograft pathology 2016 criteria. Hum Pathol 2022;127:67-77.
18. Veerankutty FH, Jayan G, Yadav MK, et al. Artificial Intelligence in hepatology, liver surgery and transplantation: emerging applications and frontiers of research. World J Hepatol 2021;13:1977-90.
19. Rueda J, Rodríguez JD, Jounou IP, Hortal-Carmona J, Ausín T, Rodríguez-Arias D. “Just” accuracy? Procedural fairness demands explainability in AI-based medical resource allocations. AI Soc 2024;39:1411-22.
20. Tsamados A, Aggarwal N, Cowls J, et al. The ethics of algorithms: key problems and solutions. AI Soc 2022;37:215-30.
21. European Parliament. Artificial intelligence liability directive. Available from: https://www.europarl.europa.eu/RegData/etudes/BRIE/2023/739342/EPRS_BRI(2023)739342_EN.pdf. [Last accessed on 30 Jul 2024].
22. Vyas DA, Eisenstein LG, Jones DS. Hidden in plain sight - reconsidering the use of race correction in clinical algorithms. N Engl J Med 2020;383:874-82.
23. Merion RM, Sharma P, Mathur AK, Schaubel DE. Evidence-based development of liver allocation: a review. Transpl Int 2011;24:965-72.
24. Uche-Anya E, Anyane-Yeboa A, Berzin TM, Ghassemi M, May FP. Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity. Gut 2022;71:1909-15.
25. Kim WR, Mannalithara A, Heimbach JK, et al. MELD 3.0: the model for end-stage liver disease updated for the modern era. Gastroenterology 2021;161:1887-95.e4.
26. Asrani SK, Jennings LW, Kim WR, et al. MELD-GRAIL-Na: glomerular filtration rate and mortality on liver-transplant waiting list. Hepatology 2020;71:1766-74.
27. Nagai S, Nallabasannagari AR, Moonka D, et al. Use of neural network models to predict liver transplantation waitlist mortality. Liver Transpl 2022;28:1133-43.
28. Wood NL, VanDerwerken D, Segev DL, Gentry SE. Correcting the sex disparity in MELD-Na. Am J Transplant 2021;21:3296-304.
29. Park R, Lee S, Sung Y, et al. Accuracy and efficiency of right-lobe graft weight estimation using deep-learning-assisted ct volumetry for living-donor liver transplantation. Diagnostics 2022;12:590.
30. Yang X, Park S, Lee S, et al. Estimation of right lobe graft weight for living donor liver transplantation using deep learning-based fully automatic computed tomographic volumetry. Sci Rep 2023;13:17746.
31. Reyes J, Perkins J, Kling C, Montenovo M. Size mismatch in deceased donor liver transplantation and its impact on graft survival. Clin Transplant 2019;33:e13662.
32. Pettit RW, Marlatt BB, Corr SJ, Havelka J, Rana A. nnU-net deep learning method for segmenting parenchyma and determining liver volume from computed tomography images. Ann Surg Open 2022;3:e155.
33. Mathur AK, Schaubel DE, Gong Q, Guidinger MK, Merion RM. Sex-based disparities in liver transplant rates in the United States. Am J Transplant 2011;11:1435-43.
34. Briceño J, Calleja R, Hervás C. Artificial intelligence and liver transplantation: Looking for the best donor-recipient pairing. Hepatobiliary Pancreat Dis Int 2022;21:347-53.
35. Ayllón MD, Ciria R, Cruz-Ramírez M, et al. Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. Liver Transpl 2018;24:192-203.
36. Mazzaferro V, Regalia E, Doci R, et al. Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis. N Engl J Med 1996;334:693-9.
37. Soubrane O, Scatton O. The Development of Transplant Oncology May Worsen the Liver Gap and Needs New Technical Options in Liver Transplantation. Ann Surg 2024;279:226-7.
38. Mehta N, Bhangui P, Yao FY, et al. Liver transplantation for hepatocellular carcinoma. Working group report from the ILTS transplant oncology consensus conference. Transplantation 2020;104:1136-42.
39. Tabrizian P, Holzner ML, Mehta N, et al. Ten-year outcomes of liver transplant and downstaging for hepatocellular carcinoma. JAMA Surg 2022;157:779-88.
40. Qu WF, Tian MX, Lu HW, et al. Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation. Hepatol Int 2023;17:927-41.
41. Kim HY, Lampertico P, Nam JY, et al. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B. J Hepatol 2022;76:311-8.
42. Tran BV, Moris D, Markovic D, et al. Development and validation of a REcurrent Liver cAncer Prediction ScorE (RELAPSE) following liver transplantation in patients with hepatocellular carcinoma: analysis of the US Multicenter HCC Transplant Consortium. Liver Transpl 2023;29:683-97.
43. London AJ. Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep 2019;49:15-21.
44. Starke G, De Clercq E, Elger BS. Towards a pragmatist dealing with algorithmic bias in medical machine learning. Med Health Care Philos 2021;24:341-9.
45. Lucivero F. Big data, big waste? A reflection on the environmental sustainability of big data initiatives. Sci Eng Ethics 2020;26:1009-30.
46. National Institute for Health and Care Research Global Health Research Unit on Global Surgery. Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries. Br J Surg 2023;110:804-17.
47. Filipponi F. Liver transplantation: is it a sustainable practice? Dig Liver Dis Suppl 2011;5:6-9.