Abstract

The process of organ allocation for transplantation is complex, involving numerous factors such as blood type, human leukocyte antigen (HLA) matching, organ size, waiting time, and geographical distance. Traditional allocation systems often face challenges in efficiently managing this complexity, potentially leading to suboptimal matches and prolonged waiting times. Artificial intelligence (AI) offers promising solutions to optimize organ matching and improve transplant outcomes. This article explores the current applications of AI in organ allocation, focusing on machine learning algorithms, predictive modeling, and decision support systems, while also addressing the associated challenges and future directions.