Explainable Artificial Intelligence with Blockchain Audit Trails for Multi-Institutional EHR-Based Organ Transplant

Abstract

Precise and coherent transplant predictions are hampered by numerous concerns with recent electronic Health Records medical institutions, involving data crumbling, privacy hazards, and unpredictable data features. To predict transplant decisions using distributed electronic health records, this research sponsors a distinctive paradigm XAI-BFL model that relates explainable artificial intelligence, authorization blockchain and federated learning approaches. Concerning blockchain transactions, the approximate convinces transparent audit trails, declares for cross- organization model refinement, and protects patient privacy. Experiments trained on a pretend multi-institutional dataset direct improved auditability, interpretability, and prediction accuracy. The envisioned framework was judged using standard category metrics and ROC curve testing to measure predictive performance. Experimental outcomes determined substantial improvements, achieving 93.8% accuracy, 93.1% precision and 92.6% recall. Comparative assessment against baseline Artificial Intelligence models, standalone AI, and federated learning advances exhibited that the recommended XAI-BFL model transfers superior organ-transplantation prediction effectiveness. The solutions point to the recommended framework as a privacy-preserving, and coherent medical decision-support tool.