AI-Driven Optimization of Kidney Allocation: Enhancing Precision in Donor-Recipient Matching

The growing demand for kidney transplant amid persistent organ scarcity demands improved donor -recipient compatibility assessment. Traditional allocation systems relying on rigid scoring fail to capture complex multidimensional data affecting posttransplant outcomes like graft rejection and delayed graft function. Artificial intelligence offers transformative potential through predictive analytics and adaptive learning. This study introduces OkAP, an AI -based system designed to enhance kidney transplant matching efficiency and accuracy by integrating clinical, genetic, and socioeconomic variables for dynamic, equitable decision -making.

We developed a novel AI framework employing 5 machine learning architectures (artificial neural networks, support vector machines, random forests, deep learning, and Bayesian belief networks ) that have been benchmarked across synthetic and real -world transplant registry datasets. We used a proprietary ranking algorithm to dynamically prioritize recipients based on multidimensional variables, including HLA compatibility, immunological risk, and geographic equity.

The deep learning model achieved 98.7 % mean accuracy across all test environments. The prioritization algorithm improved match precision by 23 % compared with traditional Jaccard similarity metrics. This approach showed a potential to reduce rejection risks, minimize complications, and improve survival rates while enabling adaptation for other organs.

The OkAP system successfully addressed limitations of conventional allocation protocols through adaptive, multidimensional compatibility modeling. A 3 -tiered framework ensuring ethical governance, electronic health record interoperability, and multicenter validation will guide clinical implementation. Future federated learning approaches will enable collaborative data sharing while preserving privacy, advancing a patient -centric future for transplant care.