In response to this, recent research in Mihoubi et al. (2020) focused on applying swarm intelligence methods to treat localization as an optimization problem. To this end, the authors presented the Enhanced Fruit Fly Optimization Algorithm (EFFOA), specifically designed to improve localization accuracy in Wireless Sensor Networks (WSNs). By leveraging iterative optimization strategies, EFFOA demonstrates a strong ability to estimate the positions of unknown nodes with high precision, effectively navigating the search space and converging toward optimal solutions. This advancement significantly contributes to the ICT framework within IoT systems, where dependable node positioning is crucial for enabling real-time decision-making and optimal system performance. In addition, the work in Miloud et al. (2019) developed an approach for node localization using the Moth Flame Optimization Algorithm (MFOA). The algorithm estimates node positions by utilizing Euclidean distance as the fitness criterion within the optimization framework.
- By providing a secure and transparent way to store and share patient information, blockchain addresses data breaches and ensures compliance with regulations.
- AI models can be trained using supervised learning algorithms, which can effectively classify and identify these diseases.
- EHealth will process information about patients at various levels, including their DNA, blood cells, and organ activity, using a combination of wearable and microfluidic biosensors.
- Cybersecurity threats and vulnerabilities pose a significant risk to the healthcare system, as interconnected medical IoT devices are susceptible to attacks.
- The work (Karatas et al. 2022) demonstrated how AI-enabled medical equipment and blockchain can facilitate RPM, improve data management, and enable early disease detection, hence optimizing patient care.
Blockchain Medical Records
However, these devices often have different communication protocols, making integration challenging. Furthermore, the variety of devices and their communication technologies creates connectivity challenges. Networks should be non-intrusive and allow for sharing resources or infrastructure (Kraemer et al. 2017). Edge and fog computing minimize latency by processing data closer to the source, providing faster response times that are critical for remote patient monitoring. While cloud computing provides greater scalability and centralized analytics, edge-based solutions are often more effective in time-sensitive clinical scenarios, making them a strong complement to traditional models (Wang et al. 2025). The EHR technique is a popular method for retaining patient encounter data, including demographics, diagnoses, medical history, medications, lab results, radiological images, prescriptions, and clinical notes (Shickel et al. 2017).
In addition, ambient energy sources, such as perovskite solar cells harvesting indoor and outdoor light, can recharge batteries for daytime operation (Min et al. 2023). Meanwhile, existing power supplies, such as smartphone-emitted electromagnetic radiation, can power and communicate with distributed sensor nodes, thereby enhancing sensing fidelity and reducing maintenance needs. In connected healthcare systems, maintaining strong data security often introduces latency due to the need for encryption, authentication, and access controls. While these measures are essential to protect sensitive patient information, they may conflict with the need for real-time responsiveness in critical care. Therefore, system designers must carefully balance robust protection with performance efficiency, ensuring patient safety without compromising data confidentiality.
How does Smart Healthcare Systems improve urban living?
This research area is still in development and requires further investigation to identify more feasible solutions. DL and ML methods need to be used to secure IoT devices in healthcare systems, which must be developed to be more robust against adversarial attacks. Furthermore, applying more advanced security algorithms is crucial, which requires further investigation. A cloudlet is a small-scale cloud data center or server cluster situated closer to end-users or devices https://bndknives.com/Spyderco/spyderco-knives-made-in-china at the network’s edge. Although data processing and application execution are closer to the point of need, cloudlets aim to minimize latency and enhance network performance, typically in mobile or edge computing environments.
3.5 Review papers focusing on computing and data management
Cooperative data fusion at the edge, for example, can extract useful information from heterogeneous and varied data sources. Based on vital signs gathered from multiple sources, this method can provide a thorough picture of a patient’s health status in medical applications. Unlike narrow AI, which is designed for specific tasks, AGI aims to exhibit general cognitive abilities, enabling it to handle diverse and complex activities across various domains within healthcare. Developing more universal explanation methods that are adaptable to various contexts and user needs could improve the generalizability of XAI techniques. Recent years have seen the rise of complex decision systems, such as Deep Neural Networks (DNNs) (Alzubaidi et al. 2024). The success of DL models, including DNNs, is due to efficient learning algorithms and their vast number of parameters.
- Innovations in the Internet of Things (IoT), artificial intelligence (AI), robotics, and data analytics are driving this significant transformation in the healthcare industry.
- Since medical data are in digital form, to exploit these data in smart healthcare systems, they are numerous challenges to conquer.
- In healthcare systems, IoT serves as a bridge between doctors and patients, providing remote access that enables doctors to monitor patients and offer remote consultations continuously.
- “We do try to provide a lot of education to our patients about their condition, what to expect from procedures and their recovery process; a lot of that type of education could be provided on the television screen as well,” Stanton says.
- Nearly half (48%) of cases had no marks with AI-CAD, compared to only 17% with conventional CAD.
- The FDA and other regulatory bodies require extensive testing and validation before approving AI systems for use in surgery, which can be time-consuming and costly 56,58.
Services
The CGM provides blood glucose readings every 5 minutes (288 per day), allowing Matilda to track her blood glucose levels and trends. Previous research has shown that self-monitoring of blood glucose is an integral component of effective treatment planning for many patients with diabetes taking insulin 41,42. The fitness tracker collects data on her activity levels, heart rate, sleep time, and location of her daily activities.
This framework encompassed the development of low-complexity traffic prediction models for slices, resource allocation models, and enforcement of service-level agreements utilizing constrained deep learning techniques. The aim of the research was to efficiently manage resources in a manner that meets service-level agreement requirements, leveraging big data analytics to improve performance and reliability. The work in Ahad et al. (2020) explained the challenges posed by the vast and diverse data generated by healthcare applications.
The main challenge in using AI for diagnostics is ensuring transparency and explainability (Figure 3). Many AI models are complex and difficult to interpret, making it challenging for healthcare professionals to trust their recommendations. Ethical issues also arise when relying too heavily on AI for decision-making, particularly in cases where the model’s recommendations may be incorrect or biased 51. These issues need to be addressed through rigorous validation, transparency, and clear regulations 55,56. A systematic literature search was conducted across major scientific databases, including PubMed, IEEE Xplore, Google Scholar, Scopus, and Web of Science. The primary aim was to identify peer-reviewed studies, reviews, case studies, and industry reports related to AI applications in healthcare.
Cyber Attack on IoT-Based Smart Healthcare System Using Machine Learning Techniques
These systems utilize renewable energy sources, such as solar, wind, or energy harvesting, to power themselves. Future research should also focus on several key aspects of electromagnetic nanonetworks, including channel modeling for specific environments such as in-body to out-of-body channels for implantable electronics and intrachip to interchip links. Future research should also explore multiple frequency bands, such as microwave, terahertz, and optical. Important topics include waveform and wavefront design, modulation and coding, interference modeling, node discovery, and medium access control solutions. Performance evaluation metrics, such as capacity, latency, and reliability, as well as novel key performance indicators specific to nanonetworks, are essential.
The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions
Spectrum IQ Infusion Systems wirelessly update the fleet’s drug library, helping to ensure that programming is based on the most up-to-date drug information. Furthermore, access to FDB Infusion KnowledgeTM allows for a pre-populated, evidence-based drug library to be used as a starting point when building a smart pump drug library, simplifying and expediting drug library creation and maintenance. Let Eko help you detect signs of disease with confidence, elevate your day-to-day exams, and provide exceptional patient care. Like smart TVs, digital door signs also connect to sensors in the hospital’s indoor positioning system. Smart hospital beds can integrate with several other monitoring technologies in a patient room, including temperature settings and speaker controls, Koczka says.
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