The work in Alotaibi et al. (2020) reviewed big data analytical systems and their role in healthcare and supply chain management. The work in Karatas et al. (2022) demonstrated the advantages and opportunities offered by big data analytics, highlighting its pivotal role in revolutionizing information gathering, sharing, and utilization within healthcare operations. Section 2 discusses the sensors and wearable devices that can be used for RPM in smart healthcare systems. This includes a discussion related to telemedicine, eHealth, mHealth, connected health, and AAL. Section 4 explains the use of EHRs in hospitals and clinics and explains their role in enhancing patient care by reducing errors and improving care coordination. Section 5 describes the technologies used for efficient communication and networking in smart healthcare systems.
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Wired or wireless sensors 5 connected to the patient’s body acquire securely patient data and at the same time physicians have access to data; thus, it provides ease for decision making. IoT applications in the healthcare sector benefit everyone to access medical help remotely, enhance the duration of therapy, and affordable cost. Digital health refers to the use of technologies, including information and communications technology, the internet, and pervasive sensing, to transform health systems and health care delivery 11,12. Within the health care system, telemonitoring and telemedicine provide remote diagnostics and treatments, electronic medical records hold patient data, and algorithms support clinicians with evidence-based decisions 13. Advanced data processing, such as activity recognition using classifiers powered by machine learning, allow large amounts of health data to be processed in real-time to make sense of people’s lifestyle and activity levels. Examples of technologies used in research-based digital health interventions are presented in Figure 1.
Technologies for Smart Health
Furthermore, healthcare institutions must address patients’ common questions, provide education, and offer support, all of which enhance patient satisfaction and alleviate the workload for medical staff 33. With healthcare data growing at an exponential rate, there is a pressing need to organize and manage information efficiently 34. The World Health Organization (WHO) has highlighted that inadequate data management can lead to critical errors, delays in patient care, and increased healthcare costs 35.
By addressing both the possibilities and limitations of AI, this paper aims to provide a comprehensive understanding of how AI can revolutionize healthcare in the coming years (Figure 1). Encryption is a specific technique of cryptography that involves converting plaintext (readable data) into ciphertext (unreadable data) using an algorithm and a key (Sasikumar And Nagarajan 2024). This process ensures that only authorized parties with the correct key can decrypt and access the original data. For data in IoT networks in the healthcare system to remain secure, https://shu-i.info/incredible-lessons-ive-learned-about-12/ private, and intact, encryption and data security are essential. Weak storage procedures and insufficient data encryption are additional risks to the healthcare system.
Furthermore, research into quantum key distribution and quantum-safe cryptographic algorithms is crucial for ensuring the highest levels of security in the transmission of sensitive data. The work in Alhaj et al. (2022) examined the use of IoT in healthcare systems, emphasizing the necessity to look into and resolve security concerns. The work in Mishra and Singh (2023) explained the challenges facing IoT in healthcare systems, such as equitable access, privacy, security vulnerabilities, and regulatory issues. The work in Mishra and Singh (2023) discussed how to develop a healthcare architecture prioritizing security based on the use of advanced technologies, such as big data, cloud computing, AI, and blockchain. The authors in Khan et al. (2023) proposed a secure IoT-based healthcare system using transfer learning techniques.
A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies
Edge learning can provide more reliable performance in healthcare critical applications, where it can continue to perform a function even when the device is offline or has a poor internet connection. For example, distributing the computational load across multiple edge devices can help scale the system more effectively than relying solely on central servers. The metaverse utilizes digital avatars, virtual representations such as digital twins, and interactive technologies like extended reality to support the analysis, optimization, and operation of various wireless applications (Khan et al. 2024). The metaverse is considered to be the next-generation mobile computing platform that will be widely used in the future.
1 IoT-driven smart healthcare systems
Sensors can sense, transmit, process information, and perform various tasks, such as motion detection, image sensing, voice control, environmental perception, physiological signal monitoring, and gesture recognition. The work in Tun et al. (2021) provided a http://www.lexa.ru/FS/msg13726.html detailed overview of the applications of IoT and wearable technologies for assisting RHM in elderly care, highlighting the types of data collected and devices used. The work in Kim et al. (2021) provided a review of mobile health (mHealth) applications designed to support caregivers of Alzheimer’s disease patients.
their data with
Smart power systems, often referred to as smart grids, are fundamentally driven by the integration of advanced information and communication technologies (Ghorbanian et al. 2019). This integration facilitates real-time monitoring, optimization, and control of energy flow while simultaneously generating an unprecedented volume of data from diverse sources such as smart meters, sensors, and distributed energy resources. Similarly, the healthcare sector has experienced a parallel transformation, with digital health records, wearable devices, remote monitoring systems, and connected medical equipment contributing to a vast accumulation of complex and heterogeneous data. In both domains, the emergence of big data presents not only a challenge but also a significant opportunity (Ghorbanian et al. 2019). The convergence of big data analytics within smart power systems and healthcare infrastructures allows for predictive insights, improved decision-making, and enhanced operational efficiency. Therefore, understanding and managing this data deluge becomes critical, as both sectors increasingly rely on data-driven intelligence to support resilient infrastructure and proactive care models.
Blockchain for Health Data Security
- Additionally, designing new transmission protocols and data management strategies to optimize energy consumption in IoT networks is crucial.
- The key asset of DL over ML is that it removes the preprocessing, feature extraction, and feature selection processes that are engaged in traditional machine learning.
- Furthermore, advanced ML methods can be used to enhance healthcare analytics and patient safety.
- 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.
This technology leverages AR, VR, and MR to create immersive experiences that can simulate the presence of individuals or objects in a real or virtual environment. Holographic communication is used for communication and interaction, enabling people to”be present”in a virtual space. With 6 G technology, more proactive oral guidance through holographic communication becomes feasible (Ahad et al. 2024). The metaverse for healthcare refers to a virtual environment where patients, healthcare professionals, and medical institutions interact using e.g., VR and AR.
With the advancement of big data analysis, DL can achieve significant performance improvements in scenarios involving large amounts of unsupervised data, achieving great success with vast amounts of unlabeled training data. However, when training data is limited, more powerful models are necessary to enhance learning capabilities. Therefore, designing deep models to learn from fewer training data effectively is particularly important for applications in speech recognition systems, visual recognition, and medical imaging diagnosis (Alzubaidi et al. 2024). The use of large language models (LLMs) in medical contexts raises critical concerns, particularly regarding patient privacy and data bias. These models often require access to sensitive health data, making them vulnerable to privacy breaches if not properly secured.
Medical Links
Smart healthcare applications are software’s implemented to generate, gather, maintain, and data related to both patients and health organizations. This data is utilized for performing different tasks like remote patient health monitoring 80, generating patient records, planning treatment, disease detection, sensing patient conditions, and so on. Smart healthcare systems benefit patients, physicians, guardians, healthcare centers, and insurance organizations. Various software companies offer cheap cloud storage while guaranteeing high-speed data access and taking care of backups and security. However, centralized data storage can introduce bottlenecks and especially network latency when it comes to retrieving and storing data from consumer devices.
Inside IoT Now Q1 2026: AI, SGP.32 and the Future of Connectivity
The accuracy and quality of the data should be established before training any model with the collected data. Additionally, advanced ML and AI algorithms can be utilized to enhance the accuracy and reliability of data fusion. XAI has gained popularity in recent years, but researchers have only partially explained the underlying theory of AI. Further research work is indeed needed to fully understand and interpret some technologies in IoT-based healthcare systems.