The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

作者：Zahra Mohtasham‐Amiri, Arash Heidari, Nima Jafari Navimipour, Mansour Esmaeilpour, Yalda Yazdani · 发表于：Neural Computing and Applications · 年份：2024 · DOI：10.1007/s00521-023-09366-3 · 被引用次数：171 · 研究领域：Advanced Technologies in Various Fields、COVID-19 diagnosis using AI、Smart Systems and Machine Learning

Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network , recurrent neural network , generative adversarial network , multilayer perception , and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. Af...