The reviewed papers presented above primarily concentrated on specific technologies and aspects of healthcare systems. However, despite extensive research, there remains a need to identify emerging trends and underscore the technological advancements and challenges in healthcare systems. Therefore, there is a lack of identifying what are the emerging trends and relevant technologies, which are essentially required to achieve smart healthcare systems.
Emerging Technologies Shaping Smart Healthcare Systems
Several works have investigated big data analytics and data management approaches in healthcare. For example, the work in Dimitrov (2016) reviewed the IoT and big https://livechinanews.com/how-to-obtain-medical-insurance-policy-to-visit-ukraine.html data in healthcare systems and discussed how mobile apps can enhance communications between patients and doctors over a secured connection. The work in Kraemer et al. (2017) provided a comprehensive review of fog computing for data management in healthcare systems, categorizing and discussing various application use cases. The work in Baker et al. (2017) reviewed cloud computing for data storage in the healthcare sector and presented several works that focused on improving security in the cloud server.
Innovative IoT-Based Healthcare Devices: A New Era of Patient Monitoring and Care
For example, if a temperature body sensor detects a change in the body temperature, the signal will be sent wirelessly to the specialist to sound an alarm and apply a quick treatment to the patient. The sensor can be implanted inside the human body, beneath the skin (in-body), placed close to the body (off-body), sewn into clothing (wearable), or applied to the human body as tiny patches (on-body) (Ghamari et al. 2016). On the other hand, an active sensor type needs an external energy source for its response (known as an excitation signal). Since DL algorithms analyze data according to the functioning of the brain, it is feasible to be implemented it in different real-world tasks. It is mostly applied for image, text, and speech recognition, disease detection 55, prediction, and diagnosis, drug discovery, business and management, manufacturing, bioinformatics, and natural language processing. It has been discovered that these applications are unified as a range of services and products such that clients are strangers to the intricate functioning of the model.
- Responding to COVID-19 shattered preconceptions of what it takes to create a digital-first health experience.
- The work in Williams et al. (2021) investigated the application of DL in image analysis for low-resource healthcare settings.
- This IoMT-enabled system incorporates various medical sensors and devices to facilitate real-time patient monitoring and improve clinical decision-making.
- Called the Persona IQ, this device includes smart sensors that collect and wirelessly transmit patient data such as steps taken, walking speed, stride length, and knee range of motion.
- The main components of the proposed system are smartphones, controlling devices, and household appliances/devices.
Machine learning ––neural networks and deep learning
Therefore, medical institutions can collaborate to create highly accurate prediction models by sharing encrypted datasets. While cloud computing enables centralized data management, the trend is now towards decentralized systems using distributed fog/edge computing, which is positioned between hardware and the cloud (Asghari And Sohrabi 2024). Fog/edge can extend cloud computing capabilities to be closer to the things it supports and where data is generated, to the network edge. In particular, fog/edge computing can offer processing and storage services to devices (nodes) at the network’s edge, replacing the need for all computation to be done in the cloud’s core. These characteristics make fog/edge computing ideal for various essential IoT services and applications.
Typically, in the healthcare system, data comes in various formats such as text, speech, time series signals, and images. Each of these formats offers a distinct perspective on the health and medical situations of individuals. Utilizing AI tools to handle and analyze data efficiently is crucial for managing the complexity of healthcare data.
Data availability
One of the primary goals of edge learning is to address the limitations of the limited data and processing power at each edge device. This can be achieved by utilizing mobile edge computing platforms and harnessing the vast amounts of data spread across numerous edge devices. In these systems, learning from distributed data and ensuring effective communication between the edge server and devices are critical challenges, presenting countless opportunities for future research. Quantum computing, leveraging quantum superposition and entanglement, can significantly enhance computing capabilities https://payusainvest.com/how-to-obtain-medical-insurance-policy-to-visit-ukraine.html through unitary transformations using qubits.