# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### AI-Based Optimization in Edge Environments
[56] designed an enhanced Deep Convolutional Neural Network (DCNN) model combined with an innovative Grey Wolf Optimization technique for the early detection of lung nodules using IoT-enabled healthcare monitoring platforms. This approach reduces the computational demand on edge devices while improving the accuracy and speed of lung cancer detection, illustrating the potential for advanced optimization algorithms to significantly enhance the diagnostic capabilities of edge computing systems in medical applications. [PERSON] et al. [57] propose an AI-aided multi-model pipeline to improve the quality of heart monitoring on edge IoT devices. The solutions consist of CNN, CNN-LSTM, and CNN-GRU models, which try to optimize the noise reduction and signal clarity of Magnetocardiography (MCG) data. The use of multiple deep learning models directly at the edge enhances the real-time detection and classification of arrhythmias, demonstrating a significant leap in the accuracy and reliability of remote cardiac health monitoring. [PERSON] et al. [58] evaluate distinct popular classifiers along with LogNNet for detecting COVID-19 from routine blood tests. By employing chaotic mapping techniques, the research improves classification accuracy. [PERSON] et al. [59] propose a Multi-Objective Modified Heat Transfer Search (MOMHTS) optimized with Hybrid Random Forest and deep learning classifiers to reduce false-positive rates in COVID-19 and pneumonia diagnostics on CT scans and X-rays. It showcases an impressive accuracy of 99%, with increased communication speed between edge devices. Similarly, [PERSON] et al. [60] propose the use of blockchain technology for assuring data integrity and immutability for COVID-19 vaccine administration. Optimization of throughput and scalability of the proposed blockchain system is performed, showing promising results. [PERSON] et al. [61] offer an improvement to the traditional CNN models by using two advanced optimization algorithms: Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO). Both algorithms adjust the hyperparameters to optimize the image classification task for detecting oral cancer using histopathology images. The first optimizer outperforms the latter and showcases the importance of using optimized DL models in medical image analysis. [PERSON] et al. [62] focus on the application of advanced cognitive computing techniques optimized for edge computing. The study explores how integrating these techniques with optimized wireless communication frameworks can improve real-time health monitoring and emergency response systems. [PERSON] et al. [63] address the problem of accurately estimating health parameter measurements using available commercial smart devices. They developed a multitask learning framework based on MobileNetV2, which enables simultaneous predictions of various health metrics. By transforming time-series sensor data in recurrence plots, this approach improves feature extraction and reduces the computational load on edge devices. [PERSON] et al. [64] propose a system that can handle emergency healthcare settings, in which rapid decision-making is mandatory. The proposed solution consists of combining both edge computing for processing data near the end devices, and federated learning to improve data privacy. [PERSON] et al. [65] highlight the problem of lack of personalization algorithms in healthcare environments. They propose different AI models for each stage, starting from data collection to identification in real time of common health diseases. [PERSON] et al. [66] propose an innovative platform presented using a hybrid Deep Learning model, combining the Group Method of Data Handling (GMDH) and LSTM networks, but focusing on the use of explainable AI, which helps in making the decision-making process transparent and understandable by the healthcare professionals. The goal of the hybrid approach is to improve stroke prediction by significantly reducing diagnostic time, potentially improving patient outcomes by enabling quicker and more informed decisions.