Artificial Intelligence in Liver Transplantation

Background. Advancements based on artificial intelligence have emerged in all areas of medicine. Many decisions in organ transplantation can now potentially be addressed in a more precise manner with the aid of artificial intelligence. Method/results. All elements of liver transplantation consist of a set of input variables and a set of output variables. Artificial intelligence identifies relationships between the input variables; that is, how they select the data groups to train patterns and how they can predict the potential outcomes of the output variables. The most widely used classifiers to address the different aspects of liver transplantation are artificial neural networks, decision tree classifiers, random forest, and naive Bayes classification models. Artificial intelligence applications are being evaluated in liver transplantation, especially in organ allocation, donor-recipient matching, survival prediction analysis, and transplant oncology. Conclusion. In the years to come, deep learning-based models will be used by liver transplant experts to support their decisions, especially in areas where securing equitability in the transplant process needs to be optimized.

Automated summary

Advancements in artificial intelligence have led to more precise decision-making in organ transplantation, particularly in liver transplantation. By identifying relationships between input variables and predicting potential outcomes of output variables, artificial intelligence classifiers such as neural networks and decision trees are being used to address various aspects of liver transplantation. The applications of artificial intelligence in liver transplantation include organ allocation, donor-recipient matching, survival prediction analysis, and transplant oncology. In the future, deep learning-based models will likely play a significant role in supporting liver transplant decisions, especially in optimizing equity in the transplant process.