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Artif Intell Gastroenterol. Feb 28, 2022; 3(1): 21-27
Published online Feb 28, 2022. doi: 10.35712/aig.v3.i1.21
Artificial intelligence and human liver allocation: Potential benefits and ethical implications
Marcos Mucenic, Ajacio Bandeira de Mello Brandão, Claudio Augusto Marroni
Marcos Mucenic, Liver Transplant Adult Group, Irmandade da Santa Casa de Misericórdia de Porto Alegre, Porto Alegre 90020-090, RS, Brazil
Ajacio Bandeira de Mello Brandão, Claudio Augusto Marroni, Hepatology, Universidade Federal de Ciencias da Saude de Porto Alegre, Porto Alegre 90050-170, RS, Brazil
Author contributions: de Mello Brandão AB wrote about liver allocation; Marroni CA wrote about artificial intelligence; Mucenic M wrote about the applications of artificial intelligence on liver allocation and on the prediction of waiting list mortality, wrote abstract and conclusions, and revised the writing style; all three writers revised the paper.
Conflict-of-interest statement: There is no conflict of interest associated with any of the authors of this manuscript
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Marcos Mucenic, MD, PhD, Doctor, Medical Assistant, Liver Transplant Adult Group, Irmandade da Santa Casa de Misericórdia de Porto Alegre, Av. Independencia, 75, Porto Alegre 90020-090, RS, Brazil. mmucenic@gmail.com
Received: December 24, 2021
Peer-review started: December 24, 2021
First decision: January 26, 2022
Revised: February 13, 2022
Accepted: February 23, 2022
Article in press: February 23, 2022
Published online: February 28, 2022
Processing time: 62 Days and 12.5 Hours
Abstract

Since its implementation almost two decades ago, the urgency allocation policy has improved the survival of patients on the waiting list for liver transplantation worldwide. The Model for End-Stage Liver Disease score is widely used to predict waiting list mortality. Due to some limitations related to its use, there is an active investigation to develop other prognostic scores. Liver allocation (LA) entails complex decision-making, and grafts are occasionally not directed to the recipients who are more likely to survive. Prognostic scores have, thus far, failed to predict post-operatory survival. Furthermore, the increasing use of marginal donors is associated with worse outcomes. Adequate donor-recipient pairing could help avoid retransplantation or futile procedures and reduce postoperative complications, mortality, hospitalization time, and costs. Artificial intelligence has applications in several medical fields. Machine learning algorithms (MLAs) use large amounts of data to detect unforeseen patterns and complex interactions between variables. Artificial neural networks and decision trees were the most common forms of MLA tested on LA. Some researchers have shown them to be superior for predicting waiting list mortality and graft failure than conventional statistical methods. These promising techniques are increasingly being considered for implementation.

Keywords: Liver transplantation; Liver cirrhosis; Artificial intelligence; Prognosis; Survival; Machine learning

Core Tip: This review discusses the ethical aspects and current advancements in liver allocation (LA). It summarizes the concept of artificial intelligence and focuses on the latest developments of machine learning algorithms as applied to predicting waiting list mortality and LA. To date, only a few research groups have published works on this field; they also wrote reviews on the subject. Our minireview offers a thorough and impartial view of the topic, and we hope this will alert other potential researchers to this promising field.