Currently the kidney organ transplantation process is a manual and cumbersome process, resulting in organ wastage and reoccurring issues in patients due to a lack of organ health screening prior to transplantation. To solve these issues in the industry, we utilize blockchain technology and artificial intelligence to create a streamlined kidney transplantation process. Utilizing blockchain technology, we automated the organ matching process, and matched donors and patients based on the same metrics that UNOS clinicians use, resulting in efficient and faster matching. Furthermore, using artificial intelligence, we created a model with perfect accuracy that screens organs pre-transplantation to predict possible risk for common kidney diseases such as chronic kidney disease(CKD), acute kidney injury(AKI), and polycystic kidney disease(PKD), to prevent recurring issues in patients, post-transplantation. Furthermore, a tapped delay line convolutional neural network provides cybersecurity by identifying valid blockchain transactions from fake patterns with 100% accuracy, ensuring full data privacy. By synergizing blockchain, AI, and cybersecurity, this research creates an efficient, secure platform that could expand patient access to life-saving transplants, prevent transplant failures, and save thousands of lives currently lost due to inefficiencies and wait times.

The authors have declared no competing interest.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Data used was acquired from publicly accessible articles from the United Network for Organ Sharing. There was no data used that required permission to acquire, and everything used can be accessed by anyone on the internet without request. Links to datasets: https://archive.ics.uci.edu/dataset/336/chronic+kidney+disease http://biogps.org/dataset/E-GEOD-30718/ https://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-1-131

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All data produced in the present work are contained in the manuscript.