摘要Decision-making based on artificial intelligence(AI)methodology is increasingly present in all areas of modern medicine.In recent years,models based on deep-learning have begun to be used in organ trans-plantation.Taking into account the huge number of factors and variables involved in donor-recipient(D-R)matching,AI models may be well suited to improve organ allocation.AI-based models should provide two solutions:complement decision-making with current metrics based on logistic regression and im-prove their predictability.Hundreds of classifiers could be used to address this problem.However,not all of them are really useful for D-R pairing.Basically,in the decision to assign a given donor to a candidate in waiting list,a multitude of variables are handled,including donor,recipient,logistic and perioperative variables.Of these last two,some of them can be inferred indirectly from the team's previous experi-ence.Two groups of AI models have been used in the D-R matching:artificial neural networks(ANN)and random forest(RF).The former mimics the functional architecture of neurons,with input layers and output layers.The algorithms can be uni-or multi-objective.In general,ANNs can be used with large databases,where their generalizability is improved.However,they are models that are very sensitive to the quality of the databases and,in essence,they are black-box models in which all variables are impor-tant.Unfortunately,these models do not allow to know safely the weight of each variable.On the other hand,RF builds decision trees and works well with small cohorts.In addition,they can select top vari-ables as with logistic regression.However,they are not useful with large databases,due to the extreme number of decision trees that they would generate,making them impractical.Both ANN and RF allow a successful donor allocation in over 80％of D-R pairing,a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease,balance of risk score,and survival outcomes following liver transplantation scores.Many barriers need to be overcome before these deep-learning-based models can be included for D-R matching.The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.