AI-Powered Real-Time Medical Diagnosis for Low-Resource Settings: A CNN-Based Edge AI and Blockchain-Integrated Solution

In low-resource settings, healthcare use and access are still very much limited by a lack of infrastructure, shortage of medical expertise, and medical connectivity. Given that, this paper presents a novel framework to have an AI-powered real-time diagnosis of medical problems we have with real Convolutional Neural Networks (CNNs), edge AI, and federated learning to customize accurate and scalable healthcare in remote areas. The proposed system consists of integrating lightweight CNN models best suited for mobile and edge devices for on-device medical image analysis which can decrease the dependency over the cloud-based processing. Through federated learning, improvement in the model is ensured without loss of privacy of patient data, and through a multi-modal diagnostic approach, which uses image, audio, and analysis based on symptoms, the accuracy is improved. The system is also based on efficient image compression and quantization techniques to enable real-time diagnosis in bandwidth-constrained environments. Also, for the safe and tamper-proof management of medical records, blockchain technology is added to the healthcare networks that can be decentralized regarding data integrity and compatibility with one another. It has been designed to work in off-grid environments powered by AI stations, solar powered enabling it to be sustainable to rural or underdeveloped regions. Diagnosis is also shown to be highly accurate, have low latency, and be adaptable for different medical conditions in experimental evaluation. It is this type of scalable, cost-effective, and privacy-preserving solution to improve healthcare accessibility and diagnostic burden in resource-limited settings that this research provides. Future work involves expanding the system for more general disease detection and deployment in a real environment.