Aakash Kharb
Vol. 16, Issue 1, Jul-Dec 2023
Abstract:
Kidney transplantation is the preferred treatment for end-stage kidney disease, yet outcomes remain constrained by organ scarcity, imperfect donor–recipient matching, variable pathology interpretation, and late detection of rejection. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for prediction and decision support across the transplant pathway, from organ allocation to long-term follow-up. However, opaque “black-box” models raise concerns about safety, bias, and trust. Explainable AI (XAI) aims to bridge this gap by making model predictions transparent and clinically interpretable.
DOI: http://doi.org/10.37648/ijrmst.v16i01.018
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