Deep learning IoT in medical and healthcare

Title
Deep learning IoT in medical and healthcare
Publication Date
2023
Author(s)
Sharma, Ashwani
Sharma, Anjali
Virmani, Reshu
Kumar, Girish
Virmani, Tarun
Chitranshi, Nitin
( author )
OrcID: https://orcid.org/0000-0002-6508-9865
Email: nchitran@une.edu.au
UNE Id une-id:nchitran
Editor
Editor(s): Harish Garg and Jyotir Moy Chatterjee
Type of document
Book Chapter
Language
en
Entity Type
Publication
Publisher
Elsevier Inc
Place of publication
London, United Kingdom
Edition
1
DOI
10.1016/b978-0-443-19413-9.00027-8
UNE publication id
une:1959.11/71738
Abstract

One of the most important aspects of human life is health. Every civilization is paying more attention to the early detection of a disease with its prevention along with implementing technology in the area of healthcare. The use of technology helps in identifying the most effective treatments for a variety of chronic illnesses. Deep learning (DL) and the Internet of Things (IoT) are rapidly expanding and playing an extremely important role in a wide range of applications such as healthcare and medical systems. Getting knowledge and useful insights from sophisticated, high-dimensional, and diverse biological data remain a major problem in healthcare transformation. The various kinds of data, such as electronic health records (EHRs) or electric medical records (EMRs), sensor data, text, and imaging, are all examples of variable, poorly documented, complex, and unstructured data that have emerged in modern biomedical research. As of now, COVID-19 has been wreaking havoc on the world since December 2019. Fortunately, the IoT is among the most significant paradigms in which artificial intelligence (AI) technology, such as big data analysis and cloud computing are playing a critical role in order to stop the spread of the COVID-19 virus. For instance, in the case of diagnosis and remote screening of COVID-19 patients, AI technology based on machine learning (ML) and DL has redefined the flow of work by having minimum contact with patients, moreover, allowing healthcare professionals to make clinical decisions in a more efficient manner, thus, helping in protecting not only patients but also healthcare professionals. Thus, in healthcare, the IoT plays a critical role in providing patients with better medical facilities as well as supporting hospitals and healthcare professionals. Because of IoT-enabled portable medical equipment, smart healthcare surveillance systems are on the rise. This chapter mainly focuses on the various approaches enabled by AI and DL and their significant applications in the field of medical and healthcare.

Link
Citation
Deep Learning in Personalized Healthcare and Decision Support, p. 245-261
ISBN
9780443194139
Start page
245
End page
261

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