Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation

Abstract

Background/Objectives: Solid organ transplantation remains a critical life-saving treatment for end-stage organ failure, yet it faces persistent challenges, such as organ scarcity, graft rejection, and postoperative complications. Artificial intelligence (AI) has the potential to address these challenges by revolutionizing transplantation practices. Methods: This review article explores the diverse applications of AI in solid organ transplantation, focusing on its impact on diagnostics, treatment, and the evolving market landscape. We discuss how machine learning, deep learning, and generative AI are harnessing vast datasets to predict transplant outcomes, personalized immunosuppressive regimens, and optimize patient selection. Additionally, we examine the ethical implications of AI in transplantation and highlight promising AI-driven innovations nearing FDA evaluation. Results: AI improves organ allocation processes, refines predictions for transplant outcomes, and enables tailored immunosuppressive regimens. These advancements contribute to better patient selection and enhance overall transplant success rates. Conclusions: By bridging the gap in organ availability and improving long-term transplant success, AI holds promise to significantly advance the field of solid organ transplantation.

Keywords: artificial intelligence; deep learning; graft failure; machine learning; solid organ transplantation.

Figure 1

Transformation of data analysis strategies. Traditional statistics are ideal for evaluating small sample tabular data, while machine learning algorithms are suited for more complex datasets and perform best when trained on large amounts of data. Deep learning allows for the analysis of both medical images and tabular data, but unlike Generative AI, it cannot produce new data.

Figure 2

AI tools for organ transplantation. The illustration shows the anatomical locations of the human kidney, heart, and liver, followed by a list of AI-based tools used in the transplantation of each organ. On the left are the kidney transplant tools, including iBox, SOTAI, OrganPredict, NephroCage, and iChoose Kidney. In the center, above the heart, are the heart transplant tools, including CARE, AI-ECG, and machine learning models for long-term prediction. On the right, above the liver, are the liver transplant tools, including graft rejection prediction and a comparison of machine learning models with traditional scoring systems.