Article Details

In the present day, the increasing adoption of digital healthcare technologies has generated vast amounts of medical data, creating opportunities for intelligent disease diagnosis while simultaneously raising concerns regarding data security, privacy and integrity. Conventional medical diagnostic systems often suffer from limitations such as lower predictive accuracy, vulnerability to data tampering and inadequate access control mechanisms. To address these challenges, this study presents a secured AI-blockchain-enabled framework for data-driven medical diagnostic systems that integrates Artificial Intelligence (AI) and blockchain technologies to enhance diagnostic performance and healthcare data security. The framework incorporates AI-driven predictive analytics for disease detection and blockchain-based mechanisms for secured data storage, integrity verification and decentralized access management. The system was evaluated using key performance metrics, including accuracy, precision, recall, F1-score, AUC-ROC, data integrity rate, and access control success rate, and was compared with a conventional diagnostic approach. Experimental results demonstrate that the AI-blockchain framework achieved an accuracy of 97.28%, precision of 97.28%, recall of 97.68%, F1-score of 97.45% and AUC-ROC of 98.25%, significantly outperforming the conventional method, which recorded 85.93%, 85.33%, 85.93%, 85.58% and 87.35%, respectively. Furthermore, the secured AI-blockchain framework improved the data integrity rate from 88.88% to 99.0% and the access control success rate from 87.93% to 99.63%, highlighting its effectiveness in securing sensitive healthcare information. The AI-blockchain framework also achieved an average performance rate of 98.21%, compared to 86.70% for the conventional method, representing an overall improvement of 11.51%. The findings demonstrate that the synergistic integration of AI and blockchain technologies provide a robust, accurate and secured platform for medical diagnosis. The framework offers significant potential for deployment in modern healthcare environments where reliable decision-making and trustworthy data management are critical.