In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency, a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model to ensure precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline.

Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on “braintumor” website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.

Using technologies such as AI and IoT for smart healthcare can be instrumental in preventing another global health crisis like the COVID-19 pandemic. There are many who rightly believe that the COVID-19 pandemic could have been prevented if all countries had prioritized public healthcare more. A collective lack of preparedness, coordination, and empathy has allowed the virus to run rampant and become the global monster we know today. While governments around the world can be held accountable for handling the contagion poorly, they can redeem themselves by ensuring that such a situation is not allowed to repeat in the future. The correct use of urban computing for healthcare data collection, sharing and analysis can prevent another global pandemic.

Internet of Medical Things (IoMT) is becoming ubiquitous with a proliferation of smart medical devices and applications used in smart hospitals, smart-home based care, and nursing homes. It utilizes smart medical devices and cloud computing services along with core Internet of Things (IoT) technologies to sense patients' vital body parameters, monitor health conditions and generate multivariate data to support just-in-time health services. Mostly, this large amount of data is analyzed in centralized servers. Anomaly Detection (AD) in a centralized healthcare ecosystem is often plagued by significant delays in response time with high performance overhead. Moreover, there are inherent privacy issues associated with sending patients' personal health data to a centralized server, which may also introduce several security threats to the AD model, such as possibility of data poisoning. To overcome these issues with centralized AD models, here we propose a Federated Learning (FL) based AD model which utilizes edge cloudlets to run AD models locally without sharing patients' data. Since existing FL approaches perform aggregation on a single server which restricts the scope of FL, in this paper, we introduce a hierarchical FL that allows aggregation at different levels enabling multi-party collaboration. We introduce a novel disease-based grouping mechanism where different AD models are grouped based on specific types of diseases. Furthermore, we develop a new Federated Time Distributed (FedTimeDis) Long Short-Term Memory (LSTM) approach to train the AD model. We present a Remote Patient Monitoring (RPM) use case to demonstrate our model, and illustrate a proof-of-concept implementation using Digital Twin (DT) and edge cloudlets.

Human activity recognition (HAR) is the method of detecting the physical activity of a person. It has a huge scope in the medical domain for supervision and health analysis. With the help of artificial intelligence, it can be performed using regularly available smartphone devices. For healthcare, HAR is often a part of an IoT framework. Using a cloud-based IoT system ensures maximum resource usage and data storage but comes with the challenges of high latency and bandwidth consumption.

Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view. In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss the limitations of current works and identify the directions for future works.

Like many countries, China has a health care problem. Changing demographics and lifestyles mean demand for health care is outstripping growth in medical resources and its cost is rising faster than the insurance premium. With 250 million people over the age of 60, the world's most populous country is ageing. Diseases associated with more affluent societies, such as cardiovascular conditions and diabetes, are on the rise. China has 400 million chronic disease patients whose treatment costs 70% of total health care resources.

The field of healthcare and medicine and specially the digital healthcare will get a great boost with the advancement and wide scale use of Quantum Computing and Artificial Intelligence. In fact, these technologies have already started transforming different areas of Healthcare and Medicine in a big way. Before even quantum computers were there, scientists at the University of Virginia School of Medicine long back anticipated the potential of quantum computers to better understand genetics and different diseases. The envision been realized and a team at the University's center for quantum computing & biology is now harnessing the power of quantum computing to gain better insights into genetic diseases with the help of machine learning algorithms. Researchers expecting that these efforts will benefit not only health care and medicine but also many other streams of science and technology.

In recent years, healthcare went through a major digital transformation. This transformation includes digitizing health records, medical records, integration between multiple patient record databases, and so on. There is a huge amount of digital transformation occurring in patient visits, appointments, follow-up, test results. Mainly the interaction and communication between patient and doctor is digitalized. It is also undeniable that in recent years, a new revolution of healthy living has occurred.

People around the world wish to talk to her or see her in real life. Sometimes listening to her interviews, her knowledge about various fields and her thought process as natural intelligence makes us forget that she is an Artificially Intelligent robotic machine; created by Hanson Robotics and an excellent example of AI, ML and Deep Learning. This robot is getting ready to revolutionize health care sector and the humanoid is already being used to help research autism and other diseases. Artificial Intelligence is ready to rule the world. Machines are becoming smarter day by day to help people.