scholarly journals Reimagining the Future of Healthcare Industry through Internet of Medical Things (IoMT), Artificial Intelligence (AI), Machine Learning (ML), Big Data, Mobile Apps and Advanced Sensors

2019 ◽  
Vol 9 (1) ◽  
pp. 3014-3019
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Science Fiction ◽  
Medical Devices ◽  
Mobile Apps ◽  
The Internet ◽  
Healthcare Industry ◽  
Remote Locations ◽  
Internet Of Medical Things

From Bluetooth enabled hearing aids to robotic caretakers, wearable and smart devices industries are immensely contributing to the development of the healthcare industry with the help of Internet of Things (IoT). Latest technologies like Artificial Intelligence, 3D Printing, Big data, Machine Learning, Advanced Sensors, Mobile Applications and other technologies will continue to generate lot of opportunities for Medtech organizations. Some of the latest healthcare innovations practiced at present might have been seen or read by some of us only in science fiction movies or science fiction stories a long ago. Presently, IoT and Artificial Intelligence is creating a revolution in healthcare industry when it comes to diagnosis and treatment of varied diseases. From smartphones to robots, artificial intelligence is already making its presence felt in healthcare industry and as such it is progressively recognizing the transformative nature of IoT technologies which drives innovation in the development of connected medical devices. Gradual increase in the number of connected medical devices with the advent of technology advancements helps to capture and transmit medical related data wherever and whenever required to the concerned people and thus, it gave birth to the Internet of Medical Things (IoMT), where the Internet of Things (IoT) and healthcare meet. The IoMT helps to constantly monitor and alter (if required) the behvaiour of the patient and his/her health status in real time and also supports healthcare organizations to effectively streamline clinical processes, patient information and related work flows to enhance its operational productivity. The IoMT has made and continues to make the delivery of P4 Medicine (Predictive, Preventive, Personalized and Participatory) even for remote locations with the help of connected sensors and devices helping in real-time patient care. IoMT helps doctors and caregivers to provide patient care and support by constantly monitoring data related to patients through mobile apps and connected medical devices even when patient(s) or doctor(s) are located at remote locations. This research paper discusses about six use cases explaining how IoMT is applied in healthcare industry.

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

scholarly journals Achieving Resilience and Business Sustainability during COVID-19: The Role of Lean Supply Chain Practices and Digitalization

2021 ◽  
Vol 13 (22) ◽  
pp. 12369
Author(s):  
Matteo Trabucco ◽  
Pietro De Giovanni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Customer Service ◽  
Mobile Apps ◽  
Production Costs ◽  
Sustainable Business ◽  
Business Sustainability ◽  
Logistic Regression Models

This paper investigates how firms can enjoy a sustainable business even during the COVID-19 pandemic. The adoption of lean coordination mechanisms over the supply chain (SC) and lean approaches in omnichannel strategies can guarantee the business sustainability and resilience. Furthermore, we investigate whether business sustainability, along with digitalization through mobile apps, Artificial Intelligence systems, and Big Data and Machine Learning enable firms’ resilience. We first explore the background on the subject, identify the research gap, and develop some research hypotheses to be tested. Then, we present the data collection process and the sample, which finally consists of firms from different sectors, including retailing, electronics, pharmaceutics, and agriculture. Several logistic regression models are developed and estimated to generate findings and managerial insights. Our results show that a lean omnichannel approach is an effective practice to preserve production costs, SC visibility, inventory available over the SC, and sales. Furthermore, lean coordination with contracts can make a business sustainable by preserving quality, ROI, production costs, customer service, and inventory availability. Finally, firms can be highly sustainable through resilience when they engage in sustainable ROI, SC visibility, and sales; in contrast, the adoption of mobile apps worsens firms’ resilience, which is not influenced by Artificial Intelligence and Big Data and Machine Learning.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Innovative Approach to Build a Scalable Hybrid Model for Prescriptive Maintenance

2021 ◽  
Author(s):  
Richard Büssow ◽  
Bruno Hain ◽  
Ismael Al Nuaimi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Hybrid Model ◽  
Domain Knowledge ◽  
Spare Parts ◽  
Good Prediction ◽  
First Principle ◽  
Plant Data ◽  
Rotating Equipment

Abstract Objective and Scope Analysis of operational plant data needs experts in order to interpret detected anomalies which are defined as unusual operation points. The next step on the digital transformation journey is to provide actionable insights into the data. Prescriptive Maintenance defines in advance which kind of detailed maintenance and spare parts will be required. This paper details requirements to improve these predictions for rotating equipment and show potential to integrate the outcome into an operational workflow. Methods, Procedures, Process First principle or physics-based modelling provides additional insights into the data, since the results are directly interpretable. However, such approaches are typically assumed to be expensive to build and not scalable. Identification of and focus on the relevant equipment to be modeled in a hybrid model using a combination of first principle physics and machine learning is a successful strategy. The model is trained using a machine learning approach with historic or current real plant data, to predict conditions which have not occurred before. The better the Artificial Intelligence is trained, the better the prediction will be. Results, Observations, Conclusions The general aim when operating a plant is the actual usage of operational data for process and maintenance optimization by advanced analytics. Typically a data-driven central oversight function supports operations and maintenance staff. A major lesson-learned is that the results of a rather simple statistical approach to detect anomalies fall behind the expectations and are too labor intensive. It is a widely spread misinterpretation that being able to deal with big data is sufficient to come up with good prediction quality for Prescriptive Maintenance. What big data companies are normally missing is domain knowledge, especially on plant critical rotating equipment. Without having domain knowledge the relevant input into the model will have shortcomings and hence the same will apply to its predictions. This paper gives an example of a refinery where the described hybrid model has been used. Novel and Additive Information First principle models are typically expensive to build and not scalable. This hybrid model approach, combining first principle physics based models with artificial intelligence and integration into an operational workflow shows a new way forward.

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