Parham Soufizadeh - Biomedical Research Institute, University of Tehran, Tehran, Iran- Gene Therapy Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
Ali PourShaban-Shahrestani - Student of Veterinary Medicine, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
Pouya Houshmand - Biomedical Research Institute, University of Tehran, Tehran, Iran- Department of Microbiology and Immunology, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
Mohammadmehdi Dehghan - Biomedical Research Institute, University of Tehran, Tehran, Iran- Department of Surgery and Radiology, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
Mahyar Mohebbi

Background and aims: Technological advances continue to evolve at a breathtaking pace. Withnear-exponential advances in computer processing capacity and the advent, progressive understandingand refinement of software algorithms, medicine, and transplantation surgery have begunto delve into artificial intelligence (AI) systems. The purpose of this review is to providean understandable summary of the applications of AI in transplantation surgery, exploring keydefinitions and basic development principles of AI technology as it currently stands.Method: This study is a narrative review of published articles in the field of AI applications intransplantation surgery. To collect the resources, the keywords of “transplantation”, “artificialintelligence” and “machine learning” were used in databases such as Google Scholar, ScienceDirect, PubMed, Wiley, and so forth.Results: A key issue in the field of transplantation is the analysis of the transplant recipient’ssurvival. Utilizing the information obtained from transplant patients will be possible to predictthe likelihood of transplantation success in other patients. Also, the major factors that affect thechance of a successful operation can be identified. AI classifiers differ in the way they establishrelationships between the input variables, how they select the data groups to train patterns, andhow they can predict the possible options of the output variables. There are six main areas oftransplantation that AI studies are focused on: radiological evaluation of the allograft, pathologicalevaluation including molecular evaluation of the tissue, prediction of graft survival, optimizingthe dose of immunosuppression, diagnosis of rejection, and prediction of early graft function.Machine learning techniques provide increased automation leading to faster evaluation and standardization.Furthermore, transplantation is fortunate to have large data sets upon which machinelearning algorithms can be constructed.Conclusion: AI is now available to improve pre-transplant management, donor selection, andpostoperative management of transplant patients and also optimize donor selection to identifypatients likely to benefit from transplantation of higher-risk organs, increasing organ discardand reducing waitlist mortality. AI and technology-enabled management tools are now availablethroughout the transplant journey. Unfortunately, those are frequently not available at the point ofdecision (patient listing, organ acceptance, post-transplant clinic), which hinders their widespreadutilization.

Transplantation, Artificial Intelligence, Big Data, Machine Learning