Cross-Modal Neural Networks for Real-Time Multisensory Information Fusion in Smart Healthcare

In the age of intelligent healthcare, fast multisensory information fusion at rates beyond real-time is crucial to perform accurate diagnosis, patient monitoring, and emergency response. However, traditional fusion techniques cannot handle heterogeneous medical data, real-time processing, or provide security to data. To integrate multimodal healthcare data that includes medical imaging, biosignals, environmental sensors, wearable data, this paper proposes an Adaptive Cross-Modal Neural Network (ACMNN), based on a new approach to building deep learning framework, a deep learning framework. To afford intermodal feature extraction and synchronization, a Latent Space Alignment (LSA) Module is designed to ensure feature matching and synchronization using a Hybrid TransformerCNN Fusion Framework with a Cross attention mechanism. A self-supervised contrastive learning module is integrated to increase robustness and to allow feature discrimination with little supervision. Moreover, to embed the privacy of medical data in the training algorithms, the model applies multi-agent reinforcement learning for dynamic decisions in general and blockchain integrated federated learning for private, decentralized training across multiple healthcare institutions. Deployment of the Edge AI on the 5G enabled devices achieves real-time processing as it decreases the latency and the resource consumption in the wearable devices. The proposal results experimentally prove its superiority as a diagnostic approach in terms of accuracy, and speed of computations, as well as its security. It enables the next generation of AI-driven healthcare systems capable of secure, real-time, and intelligent decision making for the benefits of patients as well as the operational efficiency of environments in which medical care is offered.

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