# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### AI-Based Optimization in Edge Environments
[PERSON] et al. [67] compare a vast number of architectures, from convolutional neural networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRUs) to hybrid models in the context of older adult fall prevention. Their findings indicate that the CNN-LSTM architecture, with an attention layer, performs better than the others on most relevant metrics and runs effectively at the edge on resource-constrained devices such as wearable devices. The optimal device for running of the proposed model is Raspberry Pi 3 Model B, which manages to notify the caregiver in 2 s.  
[PERSON] et al. [68] use the Yolo model to accurately detect room occupancy in AAL systems, which is further fine-tuned and adapted to detect and count the total number of people in a room. It is further hyper-tuned to adapt to different environmental conditions. [PERSON] et al. [69] leverage a Non-Intrusive Load Monitoring (NILM) technology to distinguish individual activities from aggregated energy usage in residential settings. This application of pattern recognition and motif detection to smart meter data significantly improves the precision of human activity recognition, contributing to smarter energy management and personalized smart home solutions. [PERSON] et al. [70] provide a hybrid approach that is based on a combination of deep learning and Tiny AI models to facilitate skin disease diagnosis across different hardware platforms. The achievement is decreased energy consumption and improved response times for medical diagnosis in a distributed environment. [PERSON] et al. [71] explore the application of advanced DL models to enhance the diagnosis of skin diseases across various computing environments and highlight the lack of adaptability in traditional applications. They propose models such as LeNet-5, AlexNet, and VGG16 that are equipped with self-learning capabilities to allow the system to adapt and evolve in response to new diagnostic data, thus continuously refining itself. [PERSON] et al. [72] propose a Bi-Convolutional Recurrent Neural Network (Bi-CRNN) for feature extraction and Random Forest for classification. This approach was chosen to optimize the processing of sensor data from different sources and improve the overall accuracy of Human Action Recognition (HAR). [PERSON] et al. [73] use FL and Deep Reinforcement Learning (DRL) in a unified framework with Bidirectional Long Short-Term Memory (BiLSTM) networks to manage and label large volumes of unlabeled data in smart healthcare systems. This method demonstrates a significant optimization in how sensitive health data are handled and analyzed across distributed networks, by prioritizing data privacy and minimizing bandwidth use by processing data on edge devices. The challenge described by [PERSON] et al. [74] is the lack of efficient and inadaptable ways to handle dynamic data clustering methods in IoMT applications. To combat this problem, the authors employ genetically optimized Fuzzy C-means data clustering to improve the precision and response time. [PERSON] et al. [75] describe the future of 6G edge computing frameworks and focus on the importance of Cyber-Physical Systems (CPSs) in healthcare and the need for a method to efficiently examine diverse data. The proposed solution is based on a Fuzzy sustainable, interoperable, and reliable algorithm (FSIRA) for CPS-based connect hardware. [PERSON] et al. [76] introduce the Lightweight Secure Efficient Offloading Scheduling (LSEOS) model, which is developed to optimize the execution of workflow applications across various nodes in a network. This framework not only reduces the latency in data processing but also enhances the overall security of the network. Table 3 synthesizes the approaches studied for optimizing edge systems in healthcare use cases.