Purpose of review
Classifiers based on artificial intelligence have emerged in all areas of medicine. Although very subtle, many decisions in organ transplantation can now be addressed in a more concisely manner with the support of these classifiers.
Any aspect of organ transplantation (image processing, prediction of results, diagnostic proposals, therapeutic algorithms or precision treatments) consists of a set of input variables and a set of output variables. Artificial intelligence classifiers differ in the way they establish relationships between the input variables, how they select the data groups to train patterns and how they are able to predict the possible options of the output variables. There are hundreds of classifiers to achieve this goal. The most appropriate classifiers to address the different aspects of organ transplantation are Artificial Neural Networks, Decision Tree classifiers, Random Forest, and Naïve Bayes classification models. There are hundreds of examples of the usefulness of artificial intelligence in organ transplantation, especially in image processing, organ allocation, D-R matching, precision pathology, real-time immunosuppression, transplant oncology, and predictive analysis.
In the coming years, clinical transplant experts will increasingly use Deep Learning-based models to support their decisions, specially in those cases where subjectivity is common.