• Rafael Calleja Lozano, César Hervás Martínez, and Francisco Javier Briceño Delgado.
• Liver Transplantation Unit, General and Digestive Surgery Department, Reina Sofía University Hospital, 14004 Cordoba, Spain.
• Medicina (Kaunas). 2022 Nov 28; 58 (12).

AbstractLiver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered "unbalanced." In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D-R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.

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