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      Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

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          Abstract

          Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new ‘Smart’ healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system.

          An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered.

          The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.

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          Dermatologist-level classification of skin cancer with deep neural networks

          Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)
          Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
          Article 0 comments Cited 1997 times – based on 0 reviews     Review now
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            Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

            Varun Gulshan, Lily Peng, Marc Coram … (2016)
            Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
            Article 0 comments Cited 1290 times – based on 0 reviews     Review now
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              Is Open Access

              Artificial intelligence in healthcare: past, present and future

              Fei Jiang, Yong Jiang, Hui Zhi … (2017)
              Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.
              Article 0 comments Cited 591 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Oral Biol Craniofac Res
                Journal ID (iso-abbrev): J Oral Biol Craniofac Res
                Title: Journal of Oral Biology and Craniofacial Research
                Publisher: Published by Elsevier B.V. on behalf of Craniofacial Research Foundation.
                ISSN (Print): 2212-4268
                ISSN (Electronic): 2212-4276
                Publication date PMC-release: 11 December 2021
                Publication date (Electronic): 11 December 2021
                Affiliations
                [a ]DHR-MRU, Faculty of Dental Sciences, King George's Medical University, Lucknow, Uttar Pradesh, India
                [b ]Department of Oral and Maxillofacial Surgery, Faculty of Dental Sciences, King George Medical University, Lucknow, Uttar Pradesh, India
                [c ]Department of Oral Pathology and Microbiology, Faculty of Dental Sciences, King George's Medical University, Lucknow, Uttar Pradesh, India
                Author notes
                [∗ ]Corresponding author. Department of Oral and Maxillofacial Surgery Faculty of Dental Sciences, King George Medical University, Shahmina road, Chowk, Lucknow, 226003, Uttar Pradesh, India.
                Article
                Publisher Item ID: S2212-4268(21)00140-8
                DOI: 10.1016/j.jobcr.2021.11.010
                PMC ID: 8664731
                PubMed ID: 34926140
                SO-VID: 67c96a79-6a28-4f94-adaf-a3a9a2360c18
                Copyright © © 2021 Published by Elsevier B.V. on behalf of Craniofacial Research Foundation.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 27 September 2021
                Date revision received : 9 November 2021
                Date accepted : 21 November 2021
                Categories
                Subject: Article

                Keywords: internet of medical things,healthcare devices,remote monitoring,covid 19
                Data availability:
                Keywords: internet of medical things, healthcare devices, remote monitoring, covid 19

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