This document discusses the application of artificial intelligence, internet of medical things, and blockchain technology in healthcare. Specifically, it covers:
1) How AI, IoMT, and blockchain can enhance patient outcomes, reduce costs, and improve efficiencies in healthcare.
2) Examples of current applications of these technologies, including in breast cancer diagnosis, PCOS diagnosis, and dementia detection. Machine learning algorithms are shown to outperform humans in some medical image analysis and diagnosis tasks.
3) Challenges and future research areas around implementing these technologies, such as ensuring patient privacy and data security.
The emergence of the Internet of things has attracted the attention of governments, research scholars, healthcare, and business community all over the world. In the healthcare system, the motivation for using IoT is to offer promising solutions for efficiently delivering all kinds of medical healthcare services to patients at an affordable cost. IoT could be a game changer for healthcare services. It makes it now possible to process data and remotely monitor a patient in real time. IoT is important in healthcare because of the services it provides. These services enhance the quality and efficiency of care treatments which benefit patients, doctors, nurses, and hospitals in a great way. This chapter covers various applications of IoT in healthcare and their benefits and challenges. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Applications of IoT in Healthcare" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49675.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49675/applications-of-iot-in-healthcare/matthew-n-o-sadiku
The Role of the Internet of Things in Health Care: A Systematic and Comprehen...Dr. Amarjeet Singh
The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources.
Challenges and Opportunities of Internet of Things in Healthcare IJECEIAES
The Internet of Things (IoT) relies on physical objects interconnected between each other’s, creating a mesh of devices producing information and services. In this context, sensors and actuators are being continuously embedded in everyday objects (e.g., cars, home appliances, and smartphones) thus pervading our living environment. Among the plethora of application contexts, smart Healthcare is gaining momentum. Indeed IoT can revolutionize the healthcare industry by improving operational efficiency and clinical trials’ quality of monitoring, and by optimizing healthcare costs. This paper provides an overview of IoT, its applicability in healthcare, some insights about current trends and an outlook on future developments of healthcare systems.
IoT and Mobile Application Based Model for Healthcare Management Systemijtsrd
The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things IoT has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up to date summary of the potential healthcare applications of IoT HIoT based technologies. It is necessary to develop an innovative solution in the Smart Building context that increases guests’ hospitality level during the pandemics in locations like hotels, conference locations, campuses, and hospitals. The solution supports features intending to control the number of occupants by online appointments, smart navigation, and queue management in the building through mobile phones and navigation to the desired location by highlighting interests and facilities. Moreover, checking the space occupancy, and automatic adjustment of the environmental features are the abilities that can be added to the proposed design in the future development. The proposed solution can address all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things IoT sensors. Then, storing and processing collected data in servers and finally sending the desired information to the end users. Consequently, through the integration of multiple IoT technologies, a unique platform with minimal hardware usage and maximum adaptability for smart management of general and healthcare services in hospital buildings will be created. Dr. Rajendra Kumar Bharti "IoT and Mobile Application Based Model for Healthcare Management System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51889.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/51889/iot-and-mobile-application-based-model-for-healthcare-management-system/dr-rajendra-kumar-bharti
The role of the internet of things in healthcare future trends and challengesNoman Shaikh
With recent advancements in the Internet of Things (IoT), the sector of healthcare has grown increasingly expanded. Physicians and hospital staff will execute their tasks more conveniently and intelligently thanks to the Internet of Things. There is an unparalleled possibility to improve the quality and productivity of therapies and the patient's well-being and government funding, thanks to this technology-based therapy method.
The emergence of the Internet of things has attracted the attention of governments, research scholars, healthcare, and business community all over the world. In the healthcare system, the motivation for using IoT is to offer promising solutions for efficiently delivering all kinds of medical healthcare services to patients at an affordable cost. IoT could be a game changer for healthcare services. It makes it now possible to process data and remotely monitor a patient in real time. IoT is important in healthcare because of the services it provides. These services enhance the quality and efficiency of care treatments which benefit patients, doctors, nurses, and hospitals in a great way. This chapter covers various applications of IoT in healthcare and their benefits and challenges. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Applications of IoT in Healthcare" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49675.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49675/applications-of-iot-in-healthcare/matthew-n-o-sadiku
The Role of the Internet of Things in Health Care: A Systematic and Comprehen...Dr. Amarjeet Singh
The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources.
Challenges and Opportunities of Internet of Things in Healthcare IJECEIAES
The Internet of Things (IoT) relies on physical objects interconnected between each other’s, creating a mesh of devices producing information and services. In this context, sensors and actuators are being continuously embedded in everyday objects (e.g., cars, home appliances, and smartphones) thus pervading our living environment. Among the plethora of application contexts, smart Healthcare is gaining momentum. Indeed IoT can revolutionize the healthcare industry by improving operational efficiency and clinical trials’ quality of monitoring, and by optimizing healthcare costs. This paper provides an overview of IoT, its applicability in healthcare, some insights about current trends and an outlook on future developments of healthcare systems.
IoT and Mobile Application Based Model for Healthcare Management Systemijtsrd
The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things IoT has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up to date summary of the potential healthcare applications of IoT HIoT based technologies. It is necessary to develop an innovative solution in the Smart Building context that increases guests’ hospitality level during the pandemics in locations like hotels, conference locations, campuses, and hospitals. The solution supports features intending to control the number of occupants by online appointments, smart navigation, and queue management in the building through mobile phones and navigation to the desired location by highlighting interests and facilities. Moreover, checking the space occupancy, and automatic adjustment of the environmental features are the abilities that can be added to the proposed design in the future development. The proposed solution can address all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things IoT sensors. Then, storing and processing collected data in servers and finally sending the desired information to the end users. Consequently, through the integration of multiple IoT technologies, a unique platform with minimal hardware usage and maximum adaptability for smart management of general and healthcare services in hospital buildings will be created. Dr. Rajendra Kumar Bharti "IoT and Mobile Application Based Model for Healthcare Management System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51889.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/51889/iot-and-mobile-application-based-model-for-healthcare-management-system/dr-rajendra-kumar-bharti
The role of the internet of things in healthcare future trends and challengesNoman Shaikh
With recent advancements in the Internet of Things (IoT), the sector of healthcare has grown increasingly expanded. Physicians and hospital staff will execute their tasks more conveniently and intelligently thanks to the Internet of Things. There is an unparalleled possibility to improve the quality and productivity of therapies and the patient's well-being and government funding, thanks to this technology-based therapy method.
Internet of things in public healthcare organizations: the mediating role of ...IAESIJAI
Internet of things (IoT) is a promising technology to face the challenges of COVID19 and enhance the capacities of public hospitals. However, few of the literature examined the behavioural intention (BI) of patients to use the IoT wearable health device (IoTWHD). This paper aims to examine the factor that affect the BI toward using the IoTWHD. The study proposes that variables of Technology acceptance model (TAM3) along with unified theory of acceptance and use of technology (UTAUT) can explain the BI. The population is the patients of public hospitals. Convivence sampling was deployed to collect the data using a questionnaire. 161 respondents participated in this study. The finding of Smart Partial Least Square showed that subjective norms (SN) affected the perceived usefulness (PU). Perceived enjoyment (PE) affected the perceived ease of use (PEOU). Further, PU, PEOU and perceived security (PS) affected the BI to use IoTWHD. Attitude mediated the effect of PU and PEOU on BI. More positive word of mouth are needed to enhance the perception of patients about BI to use IoTWHD in public health organizations.
Telehealth care enhancement using the internet of things technologyjournalBEEI
Chronic diseases quickly become broader public health issues because of the difficulty in obtaining appropriate, often long-term health care. So that, it requires the extension of health care for patients with chronic diseases beyond the clinic to include patient’s home and work environment. To reduce costs and provide more appropriate healthcare, we need telehealth care where internet of things (IoT) technology plays an important role. The integration of the IoT and medical science offers opportunities to improve healthcare quality, and efficiency and to better coordinate healthcare delivery at home and in the workplace. In this paper, we present the realization of a remote healthcare system based on the IoT technology. The function of this system is the transmission via a gateway of internet collected data using biomedical sensors node based Arduino board (e.g., temperature, electrical activity of the heart, heart rate monitor). These data will be stored automatically in a cloud. The health can then be monitored by the doctor or patient using a web page in real-time from anywhere at any time in the world using laptops or smart phones, etc. This method also reduces the need for direct interaction between doctor and patient.
Transforming Healthcare Industry by Implementing Cloud Computingijtsrd
In the present generation, healthcare has become the foremost imperative sector in todays medicinal eon. The massive private documents, responsive details are kept in a scalable manner. The healthcare industry has become more competitive in the digital world. As a thriving industry, its challenging for doctors to understand the moving technology in the healthcare sector. This also deals with the patient's nursing and maintains their portfolios. The overview of the project depicts a role played by the doctors, patients, management, and resource suppliers by implementing cloud technology in the healthcare industry. The platform was designed and developed for user friendly interactions where patients can connect with the management and doctors at any corner of the world. The peculiarity of the project was to withdraw the pen paper method followed by the sector for ages. Cloud computing CC has played a vital role in the project that helped and managed to store, secure large data files. The features while operating the system were QR codes, generating e mails, SMS text, and free trunk calls. This approach assists on track with each individuals health related documents, henceforward approving with the doctors to access the knowledge throughout the flow of emergency and firmly access policy. Besides the facts, it rescues the lifetime of the patients and mutually helps the doctors figure it out comfortably. The utilization of mobile aid applications may be a dynamic field and has received the attention of late. This development provides mobile technology additional enticing for mobile health m health applications. The m health defines as wireless telemedicine involving the utilization of mobile telecommunications and multimedia system technologies and their integration with mobile health care delivery systems. As well as human authentication protocols, whereas guaranteeing, has not been straightforward in light weight of their restricted capability of calculation and remembrance. Ms. Rohini Kulkarni | Pratibha Gayke "Transforming Healthcare Industry by Implementing Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46455.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/46455/transforming-healthcare-industry-by-implementing-cloud-computing/ms-rohini-kulkarni
Societal Change and Transformation by Internet of Things (IoT)CSCJournals
IoT has received much attention from scientists, industry and government all over the world for its potential in changing modern day living. IoT is envisioned as billions of sensors connected to the internet through wireless and other communication technologies. The sensors would generate large amount of data which needs to be analyzed, interpreted and utilized. Sensor will be context aware capturing device that enable modeling, interpreting and storing of sensor data which is linked to appropriate context variable dynamically. Building or home automation, social smart communication for enhancement of quality of life, that could be considered as one of the application of IoT where the sensors, actuators and controllers can be connected to internet and controlled. This paper introduces the concept of application for internet of things and with the discussion of social and governance issues that arise as the future vision of internet of things.
This paper addresses the internet of things (IoT) as the main enabling factor of promising paradigm for integration and comprehensive of several technologies for communication solution, Identification and integrating for tracking of technologies as wireless sector and actuators. This offers the capability to measure for understanding environment indicators
KARE – A Patent Protected AI based Technology into Hospital and Healthcare IIJSRJournal
Artificial Intelligence (AI) is a technology that, when linked with healthcare apps and smart wearable devices, can anticipate the onset of health issues in users by gathering and analyzing their health data. The integration of AI with smart wearable devices offers a wide range of potential applications in smart healthcare, however there is an issue with the black box operation of AI models' judgments, which has led in a lack of accountability and trust in the decisions made. In the field of healthcare, transparency, outcome tracing, and model improvement are all important. Healthcare providers can be more watchful and proactive in their interactions with patients thanks to the Internet of Things. Wheelchairs, defibrillators, nebulizers, oxygen pumps, and other monitoring devices are all tracked in real time utilizing IoT devices with sensors.
With so many health problems prevalent in today's world, maintaining personal health should be of great importance. While the number of patients is large, the number of doctors is relatively small. As a result, diagnosis is delayed and some patients are neglected. This increases the patient's confidence in the doctor for regular checkups. With all these issues in mind, healthcare systems are starting to connect to the IoT to maintain a digital identity for each patient. Many health problems are undiagnosed in the healthcare sector due to a shortage of doctors/caregivers and lack of access to healthcare. On the other hand, these IoT-based medical systems allow patients and doctors to continuously monitor and easily analyze patient data. This article provides an overview of the role of the Internet of Things in healthcare related to pandemic disease COVID-19.
Keywords: Health care, Internet of Things, pandemic, diagnosis, COVID-19.
To Cite This Article For Please use Below DOI link
DOI: 10.47750/pnr.2022.13.S07.075
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
Improving Efficiency and Outcomes in Healthcare using Internet of ThingsCitiusTech
With the adoption of cloud and big data technologies, healthcare organizations are in a position to begin experimenting with IoT. Ranging from home care to smart facilities, there are many ways in which provider organizations can benefit by using IoT in their patient care workflows. E.g., a mobile app with patient geo-fencing capabilities can help optimize physician rounds by dynamically routing the physician to the nearest patient
Payers can leverage insights generated by IoT infrastructure to improve population health, increase patient awareness and reduce healthcare costs. Payers can also design more effective reward and retention programs using IoT generated data.
As IoT is evolving, adoption is slow but steady, and investments are being made by both startups and industry leaders. Healthcare is among the top 5 industries investing in IoT.
This document discusses how IoT can be leveraged to drive efficiency in healthcare workflows and enhance clinical outcomes.
In present era, the Internet of Things (IoT) continues to gain momentum rapidly.
The Internet of Things has enormous applications in healthcare Industry from remote
monitoring to smart sensor enabled medical device integration. IoT ensures many
benefits in healthcare and has potential to improvement of patient outcomes, data
management and security in hospital, quality treatment and more. IoT technology has
huge impact on Healthcare to transform patient care and improve physician service
efficiency. The present study has focused the applications and technologies of IoT in
healthcare care sector and how far it benefits to healthcare stake holders
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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Internet of things in public healthcare organizations: the mediating role of ...IAESIJAI
Internet of things (IoT) is a promising technology to face the challenges of COVID19 and enhance the capacities of public hospitals. However, few of the literature examined the behavioural intention (BI) of patients to use the IoT wearable health device (IoTWHD). This paper aims to examine the factor that affect the BI toward using the IoTWHD. The study proposes that variables of Technology acceptance model (TAM3) along with unified theory of acceptance and use of technology (UTAUT) can explain the BI. The population is the patients of public hospitals. Convivence sampling was deployed to collect the data using a questionnaire. 161 respondents participated in this study. The finding of Smart Partial Least Square showed that subjective norms (SN) affected the perceived usefulness (PU). Perceived enjoyment (PE) affected the perceived ease of use (PEOU). Further, PU, PEOU and perceived security (PS) affected the BI to use IoTWHD. Attitude mediated the effect of PU and PEOU on BI. More positive word of mouth are needed to enhance the perception of patients about BI to use IoTWHD in public health organizations.
Telehealth care enhancement using the internet of things technologyjournalBEEI
Chronic diseases quickly become broader public health issues because of the difficulty in obtaining appropriate, often long-term health care. So that, it requires the extension of health care for patients with chronic diseases beyond the clinic to include patient’s home and work environment. To reduce costs and provide more appropriate healthcare, we need telehealth care where internet of things (IoT) technology plays an important role. The integration of the IoT and medical science offers opportunities to improve healthcare quality, and efficiency and to better coordinate healthcare delivery at home and in the workplace. In this paper, we present the realization of a remote healthcare system based on the IoT technology. The function of this system is the transmission via a gateway of internet collected data using biomedical sensors node based Arduino board (e.g., temperature, electrical activity of the heart, heart rate monitor). These data will be stored automatically in a cloud. The health can then be monitored by the doctor or patient using a web page in real-time from anywhere at any time in the world using laptops or smart phones, etc. This method also reduces the need for direct interaction between doctor and patient.
Transforming Healthcare Industry by Implementing Cloud Computingijtsrd
In the present generation, healthcare has become the foremost imperative sector in todays medicinal eon. The massive private documents, responsive details are kept in a scalable manner. The healthcare industry has become more competitive in the digital world. As a thriving industry, its challenging for doctors to understand the moving technology in the healthcare sector. This also deals with the patient's nursing and maintains their portfolios. The overview of the project depicts a role played by the doctors, patients, management, and resource suppliers by implementing cloud technology in the healthcare industry. The platform was designed and developed for user friendly interactions where patients can connect with the management and doctors at any corner of the world. The peculiarity of the project was to withdraw the pen paper method followed by the sector for ages. Cloud computing CC has played a vital role in the project that helped and managed to store, secure large data files. The features while operating the system were QR codes, generating e mails, SMS text, and free trunk calls. This approach assists on track with each individuals health related documents, henceforward approving with the doctors to access the knowledge throughout the flow of emergency and firmly access policy. Besides the facts, it rescues the lifetime of the patients and mutually helps the doctors figure it out comfortably. The utilization of mobile aid applications may be a dynamic field and has received the attention of late. This development provides mobile technology additional enticing for mobile health m health applications. The m health defines as wireless telemedicine involving the utilization of mobile telecommunications and multimedia system technologies and their integration with mobile health care delivery systems. As well as human authentication protocols, whereas guaranteeing, has not been straightforward in light weight of their restricted capability of calculation and remembrance. Ms. Rohini Kulkarni | Pratibha Gayke "Transforming Healthcare Industry by Implementing Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46455.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/46455/transforming-healthcare-industry-by-implementing-cloud-computing/ms-rohini-kulkarni
Societal Change and Transformation by Internet of Things (IoT)CSCJournals
IoT has received much attention from scientists, industry and government all over the world for its potential in changing modern day living. IoT is envisioned as billions of sensors connected to the internet through wireless and other communication technologies. The sensors would generate large amount of data which needs to be analyzed, interpreted and utilized. Sensor will be context aware capturing device that enable modeling, interpreting and storing of sensor data which is linked to appropriate context variable dynamically. Building or home automation, social smart communication for enhancement of quality of life, that could be considered as one of the application of IoT where the sensors, actuators and controllers can be connected to internet and controlled. This paper introduces the concept of application for internet of things and with the discussion of social and governance issues that arise as the future vision of internet of things.
This paper addresses the internet of things (IoT) as the main enabling factor of promising paradigm for integration and comprehensive of several technologies for communication solution, Identification and integrating for tracking of technologies as wireless sector and actuators. This offers the capability to measure for understanding environment indicators
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Artificial Intelligence (AI) is a technology that, when linked with healthcare apps and smart wearable devices, can anticipate the onset of health issues in users by gathering and analyzing their health data. The integration of AI with smart wearable devices offers a wide range of potential applications in smart healthcare, however there is an issue with the black box operation of AI models' judgments, which has led in a lack of accountability and trust in the decisions made. In the field of healthcare, transparency, outcome tracing, and model improvement are all important. Healthcare providers can be more watchful and proactive in their interactions with patients thanks to the Internet of Things. Wheelchairs, defibrillators, nebulizers, oxygen pumps, and other monitoring devices are all tracked in real time utilizing IoT devices with sensors.
With so many health problems prevalent in today's world, maintaining personal health should be of great importance. While the number of patients is large, the number of doctors is relatively small. As a result, diagnosis is delayed and some patients are neglected. This increases the patient's confidence in the doctor for regular checkups. With all these issues in mind, healthcare systems are starting to connect to the IoT to maintain a digital identity for each patient. Many health problems are undiagnosed in the healthcare sector due to a shortage of doctors/caregivers and lack of access to healthcare. On the other hand, these IoT-based medical systems allow patients and doctors to continuously monitor and easily analyze patient data. This article provides an overview of the role of the Internet of Things in healthcare related to pandemic disease COVID-19.
Keywords: Health care, Internet of Things, pandemic, diagnosis, COVID-19.
To Cite This Article For Please use Below DOI link
DOI: 10.47750/pnr.2022.13.S07.075
Survey of IOT based Patient Health Monitoring Systemdbpublications
The Internet of things has provided a promising opportunity and applications for medical services is one of the most important way or solution for taking care of population which is in rapid growth. Internet of things consists of communication and sensors; wireless body area network is highly suitable tool for the medical IOT device. In this survey we discuss mainly on practical issues for implementation of WBAN to health care service tool for the medical devices. The IoT applications are key enabling technologies in industries. A main aim of this survey paper is that it summarizes the present state-of-the-art IOT in industries and also in workflow hospitals systematically. In recent years wide range of opportunity and powerful of IOT applications are developed in industry. The health monitoring system is a big challenge for several researchers. In this paper introduced on the survey of different IOT applications are used for the health monitoring system. The IoT applications are used to decrease the problems which are related to health care system.
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With the adoption of cloud and big data technologies, healthcare organizations are in a position to begin experimenting with IoT. Ranging from home care to smart facilities, there are many ways in which provider organizations can benefit by using IoT in their patient care workflows. E.g., a mobile app with patient geo-fencing capabilities can help optimize physician rounds by dynamically routing the physician to the nearest patient
Payers can leverage insights generated by IoT infrastructure to improve population health, increase patient awareness and reduce healthcare costs. Payers can also design more effective reward and retention programs using IoT generated data.
As IoT is evolving, adoption is slow but steady, and investments are being made by both startups and industry leaders. Healthcare is among the top 5 industries investing in IoT.
This document discusses how IoT can be leveraged to drive efficiency in healthcare workflows and enhance clinical outcomes.
In present era, the Internet of Things (IoT) continues to gain momentum rapidly.
The Internet of Things has enormous applications in healthcare Industry from remote
monitoring to smart sensor enabled medical device integration. IoT ensures many
benefits in healthcare and has potential to improvement of patient outcomes, data
management and security in hospital, quality treatment and more. IoT technology has
huge impact on Healthcare to transform patient care and improve physician service
efficiency. The present study has focused the applications and technologies of IoT in
healthcare care sector and how far it benefits to healthcare stake holders
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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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Nevertheless, ever since the concept of IoT was first presented to the public in 1999, the
function of IoT has gotten progressively engaged in all sectors of the Internet of Everything
(IoE) that exists in the modern day. The term Internet of Medical Things (IoMT) is an
abbreviation for IoT-based technology applied in the medical domain, which serves as a
fundamental element of the ongoing innovation and transformation within the healthcare
industry. Connecting inexpensive sensors to the Internet, the IoT records events in the real
world and facilitates the management of physical infrastructures. This has profound
implications for human life. Personal companion, smart refrigerators, smart fire system, road
signal, room temperature controls, smart watches, smart health meter, etc. are all real-world
applications of IoT. This is because of the numerous advantages that its services provide to
people in all walks of life. The Internet of Things (IoT) promotes time savings in routine tasks
by facilitating greater connectivity; for instance, Amazon, Alexa, and Apple homepod can be
used to get voice-activated responses to the inquiries without turning on the PC or using the
phone. Varying sectors are adopting IoT at different rates, as evidenced by a recent poll
predicting that by 2025, 41.6 billion IoT gadgets will be in use, producing 79.4 ZB of data, and
a corresponding increase in IoT revenue from $5.6 billion in 2016 to $27 billion by 2024.
Improving health monitoring, management, and processes through the IoMT can have
a significant impact on people's health and quality of life. Disease prevention, lower healthcare
expenses, and guaranteeing a good quality of life are all bolstered by the progress of enhanced
low-cost advanced healthcare infrastructure and solutions, as anticipated by WHO by the year
2030. By 2050, the world's population is expected to grow by 31 percent to 9.8 billion as a
result of longer life spans. Because of the current rate of population growth, by 2050, 16% of
the world's people will be 65 years of age or older.
Complex Internet of Things technologies are included in IoMT. These technologies
include radio frequency recognition technology, sensor technology, and location tracking
technology [3]. IoMT provides several services, including identity identification, remote
monitoring of vital signs, medication administration tracking, and equipment tracking.
Recently, the use of wireless medical sensors, such as pressure sensors, biosensors, and
implantable sensors, has become incredibly common. The advent of 5G is expected to further
progress the healthcare sector by allowing smart health applications such as distant assessment,
operation instruction, emergency care vehicles, and portable medical gadget [4-8]. It is critical
to process and make decisions about real-time medical data quickly, while also protecting
patient privacy. The IoMT can help create a completely integrated health environment,
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connecting doctors and patients through various medical signals such as ultrasounds, blood
pressure, glucose receptors, EEGs, and ECGs [9]. The healthcare industry has benefited greatly
from the development of machine learning and deep learning, as well as the high speed
provided by WLAN technologies, allowing medical professionals to deal with various formats
of medical data from the same patient simultaneously, improving disease diagnosis and
prediction, and ultimately improving patient outcomes [10-14].
Blockchain technology is also making significant progress in the distribution of
healthcare records, and it guarantees data safety and anonymity by storing data in decentralized
locations. Each member of the network has access to the same set of records, making it a
distributed ledger that can also be used to speed up and automate the old procedure, and save
money. In the future, a tool that combines and shows all current medical information on a
person's medical condition in a secure healthcare environment could contribute to personalized
healthcare that is reliable and secure [15, 16].
Figure 1. Layered architecture of IoMT [3]
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IoMT
IoMT follows the typical 3-layer structure of IoT applications, which includes the
perception layer, the network layer, and the transmission layer, among other things. Figure 1
illustrates the IoMT design. The network layer has two sublayers: (i) Network transmission
layer, (ii) Service layer. The IoMT's network transfer layer mimics the nervous system of a
person by enabling dependable, accurate, and real-time data transfer from the perception layer.
The service layer unifies disparate networks, data formats, data warehouses, and other sources
of information. This establishes an application support platform. This platform lets a third party
construct medical apps. The application layer is subdivided into two layers: the medical
information application layer and the medical information decision-making application layer
[17-20].
IoMT-integrated technologies for building Smart Healthcare
Virtual Reality, Mixed Reality, and Augmented Reality have four primary uses:
medical and therapeutic, entertainment, commercial and industrial, and education and training.
Virtual reality technology creates a 3D world that can simulate reality, producing a sense of
"presence". Therapeutic virtual reality involves wearing a head-mounted display that creates a
realistic environment, which can treat mental health conditions, manage strokes and pain, and
prevent obesity. It can also be used to monitor cancer patients' treatments, alleviating
psychological symptoms caused by the malignancy, and improving the patient's emotional
well-being [21]. Yahara et al. [22] reported how individuals with moderate cognitive
impairment might benefit from an immersive VR experience that evoked distant recollection.
It is possible that Virtual Reality can be an useful tool in providing supportive care and
minimizing the negative effects of the ongoing pandemic. This can be achieved by using video
chats and simulating a sense of togetherness among individuals without the need for physical
travel. Augmented reality allows users to change their perception of the current world by
superimposing computer pictures on their view of the world [23,24]. Augmented Reality (AR)
is a useful tool for training, but it can also be useful because it helps visualize ideas that can't
be seen and annotate them through navigation in a virtual environment [25].
A Virtual Reality (VR) telehealth system was created by XR-Health to help confined
patients feel less stressed and anxious while also keeping them engaged in both physical and
mental activities. "Immersive VR Education Company" is the company that invented the
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"Engage" platform for VR instruction and interaction [26]. EON Reality created a VR and AR
platform for use in quarantine situations, and a VR environment for researching COVID-19.
VR also provides simulated surgical training, allowing surgeons to practice and improve
techniques with haptic features. The XVision Augmedics system improved accuracy rates for
spinal surgeries. Oxford Virtual Reality is used to address mental health conditions [27].
4. AI in Healthcare
AI is revolutionizing the healthcare business in a variety of ways, from medication
research to clinical decision-making and patient monitoring. Medical picture analysis is one of
the most crucial applications of AI. AI systems may detect patterns in medical photos that
human doctors would overlook, resulting in faster and more accurate diagnosis. AI can also
evaluate massive volumes of data to identify possible pharmaceutical candidates, saving time
and money in drug development. AI-powered chatbots and virtual assistants serve patients by
giving individualized assistance and guidance, while population health management uses AI to
identify high-risk populations for preventative care. Artificial intelligence has the potential to
increase healthcare efficiency, accuracy, and speed while lowering costs and human error.
4.1 AI in Breast Cancer Diagnosis
Mammography screening is used to detect breast cancer, which is a primary source of
death in women. Various countries have taken various ways to screening, with some employing
clinical settings and others relying on government-run initiatives. Mammograms are examined
by radiologists for calcification clusters and soft tissue anomalies. It is critical to compare
current and prior photos in order to discover changes and improve accuracy. Recent
breakthroughs in artificial intelligence and computing have considerably improved the
automated diagnosis of breast cancer, with deep learning and convolutional neural networks
showing the ability to outperform traditional approaches in terms of accuracy. AI-based
medical picture categorization can help doctors identify and treat patients more quickly and
accurately, potentially lowering readmissions and misdiagnosed costs.
A CNN-based CAD system was developed in [28] utilizing VGG16, ResNet50, and
Inception v3 on DDSM, INbreast, and BCDR database. On the DDSM database, the suggested
system obtained accuracy of 97.4% and AUC of 99%, whereas on the INbreast database, it
achieved accuracy of 95.5% and AUC of 97.0%; and on the BCDR database, it achieved
96.60% accuracy and 96% AUC. In [29], a deep CNN and ResNet-18 were deployed for
predicting mammography breast density on 2174 images. An updated DenseNet neural
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network architecture, known as DenseNet II, was developed in [30] for the efficient and
accurate classification of benign and malignant mammography. The DenseNet neural network
model was improved, and a brand-new DenseNet-II neural network model was created to swap
out the first convolutional layer with the starting structure in the DenseNet neural network
model. Finally, the pre-processed mammogram datasets were combined with AlexNet,
VGGNet, GoogLeNet, DenseNet, and DenseNet II neural network models. The mean accuracy
of the model was 94.55%.
Improved CNN model was proposed in [31]. Hai et al. [32] recommended explicitly
differentiating abnormal degrees in digital mammograms. It suggested an end-to-end learning
algorithm based on the combination of hierarchical variables. At LASSO, guided logistic
regression is used to extract and select low-level attributes. CNNs were created in order to
extract high-level properties. A quantitative review of many classic mass detection techniques
was described in [33]. The breast masses were classified, segmented, and detected from
mammograms using deep learning methods. Methods based on ensemble learning were
proposed in [34] to identify breast cancer. The items were classified into two classes and eight
classes. An ensemble of pretrained models was offered as a solution to the classification
problem of uneven distribution. It was found that 8-class classification accuracy may be as high
as 98.5% in training and 89.5% in testing. Additionally, train and test accuracy of 99.1% and
98.2% were attained for 2 class classifications, respectively. Modified VGG (MVGG)
approach was proposed in [35] using datasets of 2D and 3D mammography images. The
suggested hybrid transfer learning model (a fusion of MVGG and ImageNet) achieved a 94.3%
accuracy in experiments.
4.2 AI in PCOS Diagnosis
Several researchers have already applied various ML models to detect PCOS. When it
comes to the diagnosis of PCOS in women, Bharati et al., relied on data-driven methods [36].
Machine learning algorithms were trained using data obtained from the Kaggle repository. A
total of 541 women were included in the dataset, 177 of whom were diagnosed with PCOS.
Data was subjected to various classification techniques, such as Random Forest (RF) and
Logistic Regression (LR), as well as a combination of the two techniques (the RFLR).
According to the findings, the top ten attributed factors accurately predicted the onset of PCOS.
Using 40-fold cross validation on the 10 most relevant features, recall and testing accuracy of
91.01 percent and 90 percent, respectively, were achieved. Bharati et al. [37] applied two types
of voting algorithm combining several ML models such as Extra Trees classifier, RF, Gaussian
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Naive Bayes (GNB), Light Gradient Boosting Machine (LGBM) for 5 and 10-fold cross-
validation. Accuracy of 91.1243% was achieved by using Voting-1 Soft (Extra Trees
Classifier, RF, GNB, LGBM) [38].
4.3 AI in Dementia Detection
A progressive neurological condition known as ‘Dementia’ affects millions of people
globally. Early dementia detection is critical for successful treatment and management of the
disease. However, dementia can be difficult to diagnose. Artificial Intelligence (AI) has shown
tremendous potential in the detection of dementia. AI algorithms can examine considerable
quantities of data to look for patterns that could indicate the presence of an illness. This includes
analyzing medical records, brain scans, and other diagnostic tests. AI can also be used to
analyze brain scans to detect changes in brain structure that may be indicative of dementia.
Machine learning algorithms can identify subtle changes in brain structure and compare them
to a database of scans from people with and without dementia to make a diagnosis.
Univariate feature selection was adapted by Bharati et al., as a filter-based feature
selection in the MR data pre-processing step [39]. Bagged decision trees were also used to
measure the most significant components in order to achieve high accuracy in categorization.
Multiple ensemble learning algorithms, including Gradient Boosting (GB), Extreme Gradient
Boosting (XGB), voting-based classification, and RF classification, were investigated for the
diagnosis of dementia. Voting-based classifiers, such as the Extra Trees classifier, RF, GB, and
XGB, were also proposed. These models were trained using an ensemble of multiple basic
machine learning models [39].
Using machine learning, So et al., suggested a two-layer model based on the method
used in dementia support centers to detect dementia early [40]. MMSE-KC data were first
categorized into normal and abnormal in the proposed model. Using CERAD-K data, the
second stage of the study was able to differentiate between moderate cognitive impairment and
dementia. In order to compare the performance of each method, Precision, Recall, and F-
measure were used with Naive Bayes, Bayes Network, Begging, Logistic Regression, Random
Forest, SVM, and Multilayer Perceptron (MLP). With an F-measure value of 0.97 for normal,
the MLP was found to be highest in F-measure values, but the SVM had an F-measure value
of 0.739 for MCI and dementia [40]. In [41], a comparative study of four machine learning
algorithms (J48, Naïve Bayes, Random Forest, and Multilayer Perceptron) was conducted. The
researchers used CFSSubsetEval to decrease the amount of attributes. The findings indicated
that, for detecting dementia, J48 outperformed all other algorithms.
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4.4 AI in Spinal Disease Classification
AI has shown potential in aiding the diagnosis of spinal diseases. Researchers have used
various machine learning algorithms, such as Support Vector Machines (SVM), LR, and neural
networks, to develop accurate and efficient models for classifying different spinal
abnormalities. In addition, feature selection and extraction techniques, such as Principal
Component Analysis (PCA) and Pearson Correlation Coefficient (PCC), have been employed
to enhance the performance of these models. For example, using feature selection, extraction,
and machine learning techniques, spinal anomalies were diagnosed in [41]. Data pre-processing
included univariate feature selection and PCA. SVM, LR, and bagging ensemble algorithms
were tested for spinal abnormality diagnosis. SVM, LR, bagging SVM, and bagging LR were
employed on 310 samples. Kaggle provided this dataset for free. SVM, LR, bagging SVM, and
bagging LR training accuracies were 86.30%, 85.47%, 86.72%, and 85.06% when 78% of the
data was used. Nonetheless, bagging SVM had the best recall and miss rate. Begum et al. [42]
used the same spinal dataset to choose the best 10, 8, 6, and 5 features based on validity or
measurement in the preliminary processing step. MLP, LR, SVM, and Bagging were used in
the classification step.
Using AI for spinal disease diagnosis has several advantages such as, the capacity to
swiftly and correctly process sizable amounts of data, providing consistent results, and
potentially reducing the time and cost of diagnosis. AI models can also learn from new data
and improve their performance over time. However, there are also challenges that need to be
addressed, such as the need for big and varied datasets to train the models and the potential for
bias in the data. Overall, AI has the potential to revolutionize spinal disease diagnosis by
providing faster, more accurate, and consistent results, leading to more effective treatments and
better patient outcomes. However, further research and development are needed to optimize
these AI models and ensure their safety and effectiveness in clinical practice.
4.5 AI in Lung Diseases Diagnosis
Faruqui et al., developed a model called LungNet, which utilizes data from CT scans
and medical IoT devices, to increase the diagnostic precision of identifying lung diseases. The
model utilizes a new 22-layer Convolutional Neural Network (CNN) to combine hidden
attributes acquired from CT scan pictures and MIoT data sources [43]. Article [44] proposed a
deep learning model for the classification of COVID-19, pneumonia, and lung cancer using a
mixture of chest x-ray and CT images. The study found that a chest CT scan is more effective
in detecting abnormalities, even in the initial phases of the disease. Various machine learning
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algorithms were tested for detecting and diagnosing lung diseases in the human lung system.
In [44], the performance of VGG19-CNN, ResNet152V2, ResNet152V2 + Gated Recurrent
Unit (GRU), and ResNet152V2 + Bidirectional GRU (Bi-GRU) were tested for detecting and
diagnosing lung diseases in the human lung system.
The emphasis of Podder et al.'s study [45] was the application of machine learning
techniques to COVID-19 diagnosis. The diagnosis was made through data analysis using a
dataset supplied by Hospital Israelita Albert Einstein in Brazil, consisting of 5644 samples with
111 variables. Standardization and processing of null values and categorical data were
performed as a pre-processing step. Next, feature selection was undertaken to identify the most
crucial characteristics for a COVID-19 diagnosis from the dataset. Several algorithms, like RF,
LR, XGBoost, and decision tree, had their kernel parameters improved [43]. Several
researchers applied different ML models for the prediction of COVID-19 patients and Intensive
Care Unit (ICU) requirements [44,45,46]. Several ML models such as K means neural network,
SVM learning algorithm, RF learner, Neural network, Adaboost had been executed in [47] in
order to evaluate the lymphography dataset.
A modified neural architecture search network (NASNet) for COVID-19 detection
using CT lung images was suggested in [48]. The research used a dataset of 3411 images and
applied the NASNet-Mobile and NASNet-Large models to it. The leftover 15% of the samples
were used for testing, with the remaining 85% being used for training. After 15 epochs, it was
discovered that NASNet-Mobile had precision, recall, and area under the receiver operating
characteristics curve (AUC) of 82.42 percent, 78.16 percent, and 91.00 percent, respectively.
In contrast, the accuracy, recall, and AUC of NASNet-Large were 81.06 percent, 80.43 percent,
and 89.0 percent, respectively. Bharati et al., introduced a modified DL approach for COVID-
19 and lung disease called Optimized Residual Network (CO-ResNet) [49]. The suggested CO-
ResNet was created by modifying the conventional ResNet 101's hyperparameters. A new
dataset of 5,935 X-ray images obtained from two public datasets was subjected to CO-ResNet
analysis [49]. There are also some research works where various deep learning methods are
applied such as CNN [50,51], CO-IRV2 [52], VGG 16 and Xception [53].
5. Blockchain in healthcare applications
The healthcare industry has unique safety and privacy requirements, as it is responsible
for safeguarding patients' medical information. With the growing use of cloud storage and
mobile gadgets for data sharing, there is a higher risk of security breaches and the potential for
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personal information to be leaked. As people seek care from multiple providers and access
health information via smart devices, there are growing concerns about the privacy and sharing
of this information. The healthcare industry must adhere to specific regulations regarding
authentication, data sharing, interoperability, medical record transfer, and mobile health
considerations.
Blockchain technology has many features that make it suitable for use in the medical
field. These capabilities are built into the system and can be used with a variety of tools and
industries. This section emphasizes the features of blockchain technology that make it an
appealing option for the healthcare sector, including security, authentication, and decentralized
storage.
Blockchain technology's decentralized storage is a key element and the foundation for
the system's improved security and authentication [54,55]. As a result of the blockchain's
ledger, decentralized storage is a technique of distributing medical records among different
computers, allowing for faster access and enhanced data quality and quantity for medical study
[56,57,58]. Blockchain technology, for instance, can be used by IoT and cloud service
providers to decentralizedly share data in a secure and discrete way. The three pillars of
blockchain technology are data accuracy, immutability, and security. The decentralized ledger
of the blockchain, which is private and confidential, can provide security by distributing data
over multiple computers instead of depending on only one point of breakdown. It is possible
to distribute a transaction around the whole blockchain network, creating a large number of
redundant data sources to validate the original transaction's validity. Because of this
redundancy, a malicious actor will be unable to make any changes to the network's data despite
altering the data on all of the other computers in the network.
The decentralized infrastructure of the blockchain also assures the authenticity of
documents and other private information recorded in blocks along the chain of blocks [59]. To
begin the process of creating, altering, or reading data recorded in the blockchain, a user must
have access to a unique key that is linked to a publicly available primary key [59]. It is possible
to keep these keys in a Bitcoin wallet, which has a corresponding Bitcoin address. Although
these software programs are mostly used for Bitcoin and cryptocurrency, the same
cryptographic approach can be used to implement other authentication processes [59]. This
authentication technique is being studied for identification and verification of identity in
everything from public records to individual medical records.
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There are a plethora of possibilities for healthcare technology in the blockchain.
Blockchain-enabled healthcare applications have been tested and documented in a number of
ways, with a few specific software solutions outlined below. OmniPHR is a methodology
designed by Roehrs, Costa, and Righi [60] to help manage Patient Health Records (PHR) across
numerous healthcare providers. The OmniPHR architecture also tackles the issue of healthcare
providers having access to the most recent patient data, even if the records are stored elsewhere
or changed by other healthcare providers. The distinction between Electronic Health Record
(EHR) and PHR is the primary issue that OmniPHR attempts to resolve. A variety of regulatory
regulations govern EHR, helping to notify the issue of standardized information guarding
across state and country lines. This keeps the records as current and accurate as feasible. Unlike
PHR, which are maintained by patients, these records are maintained by medical professionals
without any patient participation. OmniPHR helps build a framework to solve this issue, giving
patients a more complete image of their information while retaining the level of accuracy
needed by doctors [60]. Medrec is a blockchain-based decentralized record management
system for EMRs.
According to [61], the system of OmniPHR allows patients to access their medical
records in real-time from multiple healthcare providers and clinics. The system's modular
structure can connect to healthcare providers' current local data storage, enabling verification,
security, reliability, and data transmission. The system enables patients and medical
practitioners to communicate using blockchain technology. MedRec compensates medical
stakeholders with anonymized data in exchange for maintaining and securing the blockchain
network. Patients and providers can choose to share their data as metadata. The prototype is
designed to test the framework's approach and implementation before field tests. Block content
shared by members of a private P2P network represents shared data ownership and viewing
rights. Smart contracts are used to track and regulate state transitions like adding a new record
or changing viewership permissions. Patient-provider connections are logged, and viewing
permissions and data retrieval instructions are provided to medical records using the Ethereum
blockchain. The patient's data is encrypted and can be shared with doctors. An automatic
reminder is provided to the patient to review the suggested information before accepting or
declining it. A particular contract brings together all patient-provider associations, simplifying
the process of updating medical records through a single reference. Public key cryptography
and a DNS-like application are used to verify a current form of ID, such as a social security
number, in order to authenticate the patient's identity [61]. PSN stands for Personal Sensing
Network [62]. In order to fully fulfil the concept of a PSN, a component in the network must
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safely communicate its health information to other nodes. The researchers proposed two
strategies to protect a PSN-based healthcare system [62].
6. IoMT Security by Blockchain
The design of blockchains comes in two varieties, permissionless and permissioned.
Since permissionless blockchains don't need user approval, anyone is able to leave or join at
any moment. Bitcoin is an example of a permissionless blockchain that can handle a large
number of nodes even with low transaction throughput. In contrast, permissioned blockchains
have a set of rules that must be followed to join the network. Only validated blocks by the
network's miners can be added to the chain, resulting in a higher transaction throughput than
permissionless blockchains. The use of blockchain and IoMTs can provide improved visibility
and comfort for users. Blockchain can provide a powerful, open, distributed, and hard-to-
change data structure for IoT information, while IoMT devices can collect real-time sensor data
to provide a better understanding of patients' health status. These technologies can provide
benefits such as data security, improved diagnosis and treatment, and tailored healthcare
services.
7. Notable contributions performed by blockchain for the COVID-19 pandemic
During the COVID-19 pandemic, blockchain technology was applied in a number of
sectors, including medical care, banking, politics, economics, and education. Its decentralized
structure and capacity to enable safe and private data sharing have proven especially beneficial
in contact tracking, patient data sharing, disaster recovery, supply chain management,
migration supervision, automated monitoring, contactless delivery, and virtual learning.
Blockchain-based solutions have aided in the resolution of key difficulties such as preserving
data privacy, sustaining continuous supply chains, offering safe online education, and
streamlining the request and authorisation procedure for financial services. Moreover,
blockchain-powered drones and robots have been employed for automated delivery and
tracking in high-rate transmission zones. Throughout the epidemic, the usage of blockchain
technology revealed its ability to deliver efficient and effective solutions to a variety of
difficulties.
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8. Challenges and Future Research Directions
The use of IoT and AI-powered smart healthcare raises a number of ethical
considerations, including potential health disparities and privacy concerns. Patients should be
able to access and control their own health data, and adequate measures should be in place to
preserve it. Regulations and standards are required to guide the development and deployment
of these technologies, ensuring that they fulfil ethical, safety, and efficacy criteria while also
being transparent and responsible. Therefore, while these technologies have the potential to
improve healthcare outcomes significantly, resolving these problems is critical to reaching their
full promise. Denial of Service (DoS) attacks can harm patients and medical groups as well.
Resource replication (using numerous devices on the network) is a simple DoS avoidance
approach. However, it may not be practical in healthcare because some devices are implants.
New technologies and hardware problems make identifying security threats challenging. This
problem will grow as more people go online. Moreover, unsecured UI access increases risk.
9. Challenges to IoMTs Privacy and Security
The stability of the IoMT ecosystem is hindered by the absence of robust authentication
measures, and cryptographic approaches may not be suitable for resource-constrained IoMT
devices. Lightweight cryptographic solutions that consider performance limits are necessary.
The foundational components of IoMT are GPS, NFC, and RFID devices, and a versatile
routing and forwarding service is required to transfer data across multiple nodes. Traditional
fault-tolerance measures are ineffective compared to self-healing and self-adaptation, which
are crucial for IoMT systems that support health and wellness. Fault tolerance is necessary for
successful operation and fault prevention, detection, and recovery should be incorporated into
IoMT security solutions.
10. Conclusion
The development of Internet of Things communication infrastructure and physical
devices has resulted in significant advancements in remote health monitoring systems.
Combining machine learning and deep learning techniques with advanced artificial intelligence
approaches can uncover patterns that are connected to diseases and health problems. The
combination of blockchain technology with machine learning models has enabled the
development of IoT-connected health monitoring systems that enhance medical record
management, drug traceability, and the tracking of infectious diseases. To date, cutting-edge
14. Article title
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techniques for incorporating blockchain, machine learning, and deep learning into Internet of
Things applications have been developed. This paper offers a discussion of evolving Internet
of Things technologies, machine learning, and blockchain for healthcare applications.
Healthcare practitioners will be able to examine patients' clinical records on the blockchain,
and artificial intelligence will support them by using a variety of related algorithms, decision-
making tools, and a great deal of data. The medical system will benefit from the integration of
the most recent technological advancements by increasing service efficiency while
simultaneously lowering prices and democratizing healthcare. The blockchain permits the
storing of cryptographic records, which is essential for artificial intelligence.
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