ay use different technologies and may be supported by different operators. These challenges can only be satisfied through 5G networks that promise increased network capacity, interoperability, and security. Edge computing could be used to do light analytics on data before sending the data for further processing and recording on the cloud. Early processing of data compresses the traffic going to the cloud, reducing congestion, and decreasing the cloud load. Furthermore, less patient data is transferred over the network, decreasing privacy leakage. AI detection models could also be deployed on edge to detect severe conditions within the edge layer. An alert can be issued to medical users in the neigh- borhood. Blockchain provides a secure channel to share data across different parties while disallowing unauthorized access and preserving patient privacy. Smart contracts could be used along with AI models to automatically record observations and diag- noses obtained from sensor data in the patient's EHR. A full real-time emergency use case scenario is illustrated in Figure 7.6. Sensor data is sent to the nearest edge device. Data is passed to AI models on one of the MEC servers to be analyzed. Analyzed data is committed to the blockchain, which triggers a smart contract to add the patient record's new information. An alert is issued through a local blockchain that connects edge devices in the same neighborhood in case of an emergency. The alert is sent to the nearest emergency assistance system. As in [35], traffic systems are also given a notification to clear the way for an ambulance automatically. In this system, edge provides a low latency response to the emergency, and blockchain provides a channel to preserve the privacy of the patient's data and a secure method for MEC servers to share their knowledge and act. 7.8 CONCLUSION Next-generation healthcare applications require networks to have higher security, lower latencies, and a method to deal with requests of different requirements. MEC and network slicing con