Blog > The Future of Healthcare: The Smart Health System
Different healthcare systems and devices would be required to communicate seamlessly within the metaverse. Therefore, future work should focus on creating universal standards and protocols for interoperability among various healthcare technologies and platforms. High-quality VR/AR experiences in the metaverse would require substantial computing power and advanced hardware, which may not be accessible to all users. Therefore, future work should consider the development of more efficient and affordable VR/AR hardware, as well as optimizing software to run on lower-spec devices. With advancements in communication techniques, energy is expected to be delivered alongside medical analysis tasks, necessitating new transmission protocols to adjust data size and transmission rates. This approach can significantly reduce the energy dissipation of edge servers and requires re-modeling the corresponding energy queue.
One of the most common causes of elderly people’s injuries, deaths, and loss of autonomy among elderly people is an accidental fall. Fall detection sensors can help lower the death toll, the number of injuries, and the expense of healthcare systems by offering instant emergency assistance to those who have fallen accidentally. By monitoring vital signs, wearable sensors provide enormous potential for the early detection of diseases. These sensors enable data about the patients to be sent to a central management console and notify medical staff when conditions are identified remotely from the server side. The work in Rahmani et al. (2018) investigated the integration of IoT and cyber-physical systems in healthcare to develop intelligent and secure healthcare systems. Besides, the paper presented a fog computing-based solution to enhance energy efficiency, reliability, scalability, and seamless connectivity for mobile sensors in healthcare IoT systems.
Ensuring that AI outputs are explainable, clinically valid, and aligned with established medical protocols is critical. Additionally, the dynamic and unpredictable nature of real-world clinical environments makes it difficult to maintain consistent AI performance across different contexts, patient demographics, and healthcare settings. Data fusion enhances the accuracy and reliability of AI models in healthcare by integrating more effective monitoring, and develop more effectiveng graph analysis and feature visualization. These techniques address the limitations of individual data sources, improving the overall precision of AI systems and leading to more accurate disease predictions (Albahri et al. 2023). The accuracy of AI models can be considerably enhanced using data fusion techniques (Alanazi And Muhammad 2022). Data fusion involves integrating data from various sources, such as sensors, wearable devices, electronic health records (EHRs), and other inputs, to create a more comprehensive and accurate representation.
A combination of support vector machine (SVM) and dynamic time wrapping (DTW) approaches had been used for speech recognition and controlling the system Raspberry Pi board, which recorded 97% accuracy. Rizwan et al. 13 introduced an integrated smart system (i.e., deep neural network (DNN) + IoT) namely Grey Filter Bayesian Convolutional Neural Network (GFB-CNN) depending on real-time analytics for analyzing heart signals. Body sensor data REALDISP and Smartphone sensor data are used for experimental analysis of the system. Pereira et al. 15 implemented a 3D-UNet model for brain disease classification and the BRATS dataset is used and achieved 89% accuracy.
Clinical decision-making in traditional systems is often based on retrospective data and periodic check-ups, which can delay the detection of anomalies or disease progression. Emerging technologies such as digital twins offer a dynamic and real-time virtual representation of a patient’s health status. By continuously integrating sensor data and medical records, digital twins enable predictive modeling and personalized care, significantly improving response time and treatment outcomes. Furthermore, in traditional methods, decision support systems are generally rule-based or dependent on static data inputs, which can limit adaptability in rapidly evolving clinical scenarios. However, when integrating the emerging technologies, e.g., digital twins with AI and real-time data streams, this would provide dynamic simulation environments for testing interventions, optimizing workflows, and predicting outcomes more accurately.
These applications are applied across a wide range of medical disciplines, including oncology, immunology, osteopathy, and urology (Chude-Okonkwo et al. 2017). The integration of fog/edge computing and DL near data sources can lead to what is known as edge intelligence. Integrating DL into edge frameworks enhances intelligent edge capabilities, fostering mutually beneficial advancements in edge intelligence.
In general, the data collected contains socially constructed biases, inaccuracies, errors, and mistakes. This is because inaccurate or low-quality healthcare data may influence patient treatment by leading to incorrect decision-making. Therefore, finding efficient methods for data pre-processing is essential to provide high-quality input for fusion algorithms.
The work in Hussien et al. (2019) analyzed and mapped the recent technologies that use blockchain techniques in healthcare systems. Smart healthcare enhances medical service provision by integrating advanced technologies and intelligent systems within the healthcare sector. This chapter provides an overview of the concept of smart healthcare and its potential benefits by incorporating artificial intelligence, machine learning, and internet of things in early detection and diagnosis of various diseases. The advent of smart healthcare has transformed the landscape of healthcare delivery, allowing for enhanced patient care, tailored medical treatments, and streamlined healthcare administration.
While federated learning could offer considerable improvements in healthcare systems, it might come with limitations (Rauniyar et al. 2023). Federated learning requires a robust coordination infrastructure and standardization across participating entities. Therefore, continued investigation is needed to address these requirements and guide future implementations. Handling device heterogeneity, which encompasses differences in processing speed, storage capacity, and network connectivity, presents additional challenges in federated learning that must be addressed. To handle these variations effectively without sacrificing model performance, advanced algorithms and methods must be developed.
There are increasing challenges in handling multi-source and heterogeneous IoT data, especially in healthcare systems. Data fusion is a significant trend in federated learning, driven by the increasing variety and volume of data (Ji et al. 2024). Effective methodologies and algorithms are needed to address issues such as user privacy, universal model design, and stability of data fusion results.
Therefore, finding accurate and fast methods for feature extraction and classification of sensor signals is crucial. Hence, ensuring the safety and non-exposure of data sensed by WBAN https://thestrip.ru/en/for-green-eyes/letnie-chteniya-v-detskoi-biblioteke-plan-meropriyatii-otdyhaem-s-knizhkoi-letnee/ sensors to unauthorized entities and security threats is crucial. Besides, robust authentication methods and security solutions are essential, which need to be investigated (Narwal And Mohapatra 2021).
Integrating IoT technologies along with cloud services, machine learning, and deep learning techniques enhances smart healthcare services immensely 10. IoT achieved immense popularity because of its data acquiring and visualizing property through sensing the objects and communication with devices through wireless networks. IoT devices are capable of sensing, visualizing, collecting, and sharing data, and communication among the devices can be done by wireless IoT protocols like Bluetooth, ZigBee, Z-Wave, WiFi, and RFID 2, 3. These protocols play a vital role in the healthcare sector since they ensure ease and flexibility for data communication and data monitoring among employed devices. Data collected from these devices are used for different tasks like disease classification, designing, patient monitoring, and so on.