# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 5 Discussion  
As a result of our study, an important aspect of enacting edge computing in healthcare scenarios is the actual deployment and usage of edge infrastructures, which introduces serious security and privacy concerns, due to the nature of the sensitive health information collected and processed. A primary concern is data privacy, keeping patient information secure during data transmission and storage over distributed edge networks. The need for a reliable and secure authentication mechanism that can handle the decentralized nature of edge computing architectures is necessary. Considering the reported increasing adoption rate of edge computing applications, scalable and secure protocols that can manage a large amount of information from various devices are needed. To address these challenges, blockchain technology is a popular choice to enhance the security of the networks, especially due to its secure, tamper-proof data sharing characteristics. Another approach would be to include federated learning in combination with privacy-preserving techniques to allow AI models to learn and share information in a decentralized environment while keeping all the sensitive information secure. Ensuring data transfer between different layers of a network is carried out in a secure manner may be based on deploying diverse encryption techniques such as attribute-based and certificate-based techniques. Future research across security and privacy of an edge computing system should focus on adaptive solutions capable of anticipating, detecting and responding to real-time security threats. Due to the nature of health-related information, which is mostly non-integer type, the development of homomorphic encryption schemes that support floating-number operations is a hot research area. Improving the efficiency of homomorphic encryption for real-time applications should be addressed to deal with the performance limitations of resource-limited edge devices. Enhancing the scalability of blockchain solutions is essential as well; as the number of connected edge devices is continuously increasing, more efficient consensus algorithms that do not compromise security and decentralization will be needed. Another key area for exploration is cryptography solutions with quantum computing, as significant improvements in this field are occurring and may pose critical issues in future healthcare systems. Finally, zero-trust architectures designed for edge computing architectures can significantly boost the security layer, ensuring that the access is implicitly trusted withing the network. AI-optimized edge computing-based healthcare systems require real-time data processing and rapid decision-making ability. Deep learning is a key solution for the development of tailored optimization algorithms for edge architectures in healthcare. A primary objective would be to minimize the processing overhead on resource-limited edge devices, while offering improved energy efficiency. Custom lightweight AI models reduce the computational overhead, while maintaining the accuracy for tasks that require real-time monitoring and diagnostics. LLM models, such as LLaMA 3, are currently being adopted in innovative healthcare applications to support medical professionals in different scenarios. These can be adapted for the edge computing case to enhance patient-doctor interactions, AI-enabled diagnostics, patient care, etc. Furthermore, advanced techniques such as transfer learning enable pre-trained models to be fine-tuned with a limited amount of data, which is the specific case of eHealth edge environments. This not only enhances the model performance but reduces the need for extensive and resource-intensive model training, enabling real-time deployments on limited edge devices. To address more complex medical information, such as time-series medical data, hybrid architectures and optimized deep learning models show great promise for faster and more accurate analysis.