Shared Data and Anomalies Detection through the Block Chain Enabled Radiography Medical Image Sharing and Critical Notifications in King Faisal Hospital of Rwanda

Radiograph medical image sharing and result critical notifications presents several challenges in worldwide that need to be addressed to improve diagnosis, accuracy, and clinical workflow. Thus, its global impediments include such image quality and noise, resolution and detail, inconsistent image quality, data size and storage, segmentation challenges, annotation and labeling, class imbalance, interpretability and diagnostic support, generalization across populations, realtime processing and decision support and radiation exposure. This study used King Faisal Hospital (KFH) and it described the scientific analytical perspective of the block chain technology radiography medical image sharing and result critical notifications. Findings revealed that patients at 84.6% in the data shared through the block chain technology enabled radiography medical image sharing and critical notification were with anomalies while health supporters inferred that 93.3% with anomalies detected. Again, KFH leaders and medical doctors indicated that 40% of the shared were not accurate. The study inferred the block chain technology barriers such as data privacy and security pointed at 20%, scalability at 16.2 %, etc. The strategies deduced such as privacy-preserving blockchain techniques, Layer 2 Solutions and Off-Chain Storage, etc. In conclusion, this study on block chain technology in health sector is essential for medical doctors and radiologists to stay informed, innovative, and effective in digital health. It empowers the health professionals with the knowledge and skills needed to inspire the next generation and ensures that digital health remains relevant and impactful in an ever-evolving scientific landscape