IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey

Bridging healthcare gaps: a scoping review on the role of artificial intelligence, deep learning, and large language models in alleviating problems in medical deserts

"Medical deserts" are areas with low healthcare service levels, challenging the access, quality, and sustainability of care. This qualitative narrative review examines how artificial intelligence (AI), particularly large language models (LLMs), can address these challenges by integrating with e-Health and the Internet of Medical Things to enhance services in under-resourced areas. It explores AI-driven telehealth platforms that overcome language and cultural barriers, increasing accessibility. The utility of LLMs in providing diagnostic assistance where specialist deficits exist is highlighted, demonstrating AI's role in supplementing medical expertise and improving outcomes. Additionally, the development of AI chatbots offers preliminary medical advice, serving as initial contact points in remote areas. The review also discusses AI's role in enhancing medical education and training, supporting the professional development of healthcare workers in these regions. It assesses AI's strategic use in data analysis for effective resource allocation, identifying healthcare provision gaps. AI, especially LLMs, is seen as a promising solution for bridging healthcare gaps in "medical deserts," improving service accessibility, quality, and distribution. However, continued research and development are essential to fully realize AI's potential in addressing the challenges of medical deserts.

TriKSV-LG: a robust approach to disease prediction in healthcare systems using AI and Levy Gazelle optimization

A seamless connection between the Internet and people is provided by the Internet of Things (IoT). Furthermore, lives are enhanced using the integration of the cloud layer. In the healthcare domain, a reactive healthcare strategy is turned into a proactive one using predictive analysis. The challenges faced by existing techniques are inaccurate prediction and a time-consuming process. This paper introduces an Artificial Intelligence (AI) and IoT-based disease prediction method, the TriKernel Support Vector-based Levy Gazelle (TriKSV-LG) Algorithm, which aims to improve accuracy, and reduce the time of predicting diseases (kidney and heart) in healthcare systems. The IoT sensors collect information about patients' health conditions, and the AI employs the information in disease prediction. TriKSV utilizes multiple kernel functions, including linear, polynomial, and radial basis functions, to classify features more effectively. By learning from different representations of the data, TriKSV better handles variations and complexities within the dataset, leading to more robust disease prediction models. The Levy Flight strategy with Gazelle optimization algorithm tunes the hyperparameters and balances the exploration and exploitation for optimal hyperparameter configurations in predicting chronic kidney disease (CKD) and heart disease (HD). Furthermore, TriKSV's incorporation of multiple kernel functions, combined with the Gazelle optimization strategy, helps mitigate overfitting by providing a more comprehensive search space for optimal hyperparameter selection. The proposed TriKSV-LG method is applied to two different datasets, namely the CKD dataset and the HD dataset, and evaluated using performance measures such as AUC-ROC, specificity, F1-score, recall, precision, and accuracy. The results demonstrate that the proposed TriKSV-LG method achieved an accuracy of 98.56% in predicting kidney disease using the CKD dataset and 98.11% accuracy in predicting HD using the HD dataset.

Information Technology and Value-Based Healthcare Systems: A Strategy and Framework

Value-based healthcare offers a pathway for enhancing patient satisfaction and population health and reducing healthcare costs. In addition, it provides a means to enhance physicians' perception and experience in healthcare delivery. The foundation of the said system is the notion that community wellness can only be benefited when the health effects of many people are also addressed. The provision of healthcare services incurs costs. However, a value-based model addresses this issue by establishing teams that cater to individuals with similar needs. This approach fosters expertise and efficiency, ultimately leading to cost savings without rationing. Furthermore, entrusting decision-making authority regarding healthcare delivery to the clinical team enhances doctors' professionalism and the integrity of clinician-patient interactions, resulting in more effective and relevant treatments. Currently, various information technology (IT)-based solutions are the main focus for accomplishing the desired value-based healthcare system. The establishment of a coordinated framework that can help organizations create value-based healthcare systems is covered in the current article. Additionally listed are many IT-based solutions used to create a value-based healthcare system.

A fuzzy logic-based secure hierarchical routing scheme using firefly algorithm in Internet of Things for healthcare

The Internet of Things (IoT) is a universal network to supervise the physical world through sensors installed on different devices. The network can improve many areas, including healthcare because IoT technology has the potential to reduce pressure caused by aging and chronic diseases on healthcare systems. For this reason, researchers attempt to solve the challenges of this technology in healthcare. In this paper, a fuzzy logic-based secure hierarchical routing scheme using the firefly algorithm (FSRF) is presented for IoT-based healthcare systems. FSRF comprises three main frameworks: fuzzy trust framework, firefly algorithm-based clustering framework, and inter-cluster routing framework. A fuzzy logic-based trust framework is responsible for evaluating the trust of IoT devices on the network. This framework identifies and prevents routing attacks like black hole, flooding, wormhole, sinkhole, and selective forwarding. Moreover, FSRF supports a clustering framework based on the firefly algorithm. It presents a fitness function that evaluates the chance of IoT devices to be cluster head nodes. The design of this function is based on trust level, residual energy, hop count, communication radius, and centrality. Also, FSRF involves an on-demand routing framework to decide on reliable and energy-efficient paths that can send the data to the destination faster. Finally, FSRF is compared to the energy-efficient multi-level secure routing protocol (EEMSR) and the enhanced balanced energy-efficient network-integrated super heterogeneous (E-BEENISH) routing method based on network lifetime, energy stored in IoT devices, and packet delivery rate (PDR). These results prove that FSRF improves network longevity by 10.34% and 56.35% and the energy stored in the nodes by 10.79% and 28.51% compared to EEMSR and E-BEENISH, respectively. However, FSRF is weaker than EEMSR in terms of security. Furthermore, PDR in this method has dropped slightly (almost 1.4%) compared to that in EEMSR.

Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier

Obstructive Sleep Apnea (OSA) is a common sleep-breathing disorder that highly reduces the quality of human life. The most powerful method for the detection and classification of sleep apnea is the Polysomnogram. However, this method is time-consuming and cost-inefficient. Therefore, several methods focus on using electrocardiogram (ECG) signals to detect sleep apnea. This paper proposed a novel automated approach to detect and classify apneic events from single-lead ECG signals. Wavelet Scattering Transformation (WST) was applied to the ECG signals to decompose the signal into smaller segments. Then, a set of features, including higher-order statistics and entropy-based features, was extracted from the WST coefficients to formulate a search space. The obtained features were fed to a random forest classifier to classify the ECG segments. The experiment was validated using the 10-fold and hold-out cross-validation methods, which resulted in an accuracy of 91.65% and 90.35%, respectively. The findings were compared with different classifiers to show the significance of the proposed approach. The proposed approach achieved better performance measures than most of the existing methodologies.

Digital maturity and its determinants in General Practice: A cross-sectional study in 20 countries

Background

The extent to which digital technologies are employed to promote the delivery of high-quality healthcare is known as Digital Maturity. Individual and systemic digital maturity are both necessary to ensure a successful, scalable and sustainable digital transformation in healthcare. However, digital maturity in primary care has been scarcely evaluated.

Objectives

This study assessed the digital maturity in General Practice (GP) globally and evaluated its association with participants' demographic characteristics, practice characteristics and features of Electronic Health Records (EHRs) use.

Methods

GPs across 20 countries completed an online questionnaire between June and September 2020. Demographic data, practice characteristics, and features of EHRs use were collected. Digital maturity was evaluated through a framework based on usage, resources and abilities (divided in this study in its collective and individual components), interoperability, general evaluation methods and impact of digital technologies. Each dimension was rated as 1 or 0. The digital maturity score was calculated as the sum of the six dimensions and ranged between 0 to 6 (maximum digital maturity). Multivariable linear regression was used to model the total score, while multivariable logistic regression was used to model the probability of meeting each dimension of the score.

Results

One thousand six hundred GPs (61% female, 68% Europeans) participated. GPs had a median digital maturity of 4 (P25-P75: 3-5). Positive associations with digital maturity were found with: male gender [B = 0.18 (95% CI 0.01; 0.36)], use of EHRs for longer periods [B = 0.45 (95% CI 0.35; 0.54)] and higher frequencies of access to EHRs [B = 0.33 (95% CI 0.17; 0.48)]. Practicing in a rural setting was negatively associated with digital maturity [B = -0.25 (95%CI -0.43; -0.08)]. Usage (90%) was the most acknowledged dimension while interoperability (47%) and use of best practice general evaluation methods (28%) were the least. Shorter durations of EHRs use were negatively associated with all digital maturity dimensions (aOR from 0.09 to 0.77).

Conclusion

Our study demonstrated notable factors that impact digital maturity and exposed discrepancies in digital transformation across healthcare settings. It provides guidance for policymakers to develop more efficacious interventions to hasten the digital transformation of General Practice.

IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people's lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems' effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System

This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming.

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

In today's environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round.

A Versatile and Ubiquitous IoT-Based Smart Metabolic and Immune Monitoring System

In the present medical age, the focus on prevention and prediction is achieved using the medical internet of things. With a broad and complete framework, effective behavioral, environmental, and physiological criteria are necessary to govern the major healthcare sectors. Wearables play an essential role in personal health monitoring data measurement and processing. We wish to design a variable and flexible frame for broad parameter monitoring in accordance with the convenient mode of wearability. In this study, an innovative prototype with a handle and a modular IoT portal is designed for environmental surveillance. The prototype examines the most significant parameters of the surroundings. This strategy allows a bidirectional link between end users and medicine via the IoT gateway as an intermediate portal for users with IoT servers in real time. In addition, the doctor may configure the necessary parameters of measurements via the IoT portal and switch the sensors on the wearables as a real-time observer for the patient. Thus, based on goal analysis, patient situation, specifications, and requests, medications may define setup criteria for calculation. With regard to privacy, power use, and computation delays, we established this system's performance link for three common IoT healthcare circumstances. The simulation results show that this technique may minimize processing time by 25.34%, save energy level up to 72.25%, and boost the privacy level of the IoT medical device to 17.25% compared to the benchmark system.

An AI-Empowered Home-Infrastructure to Minimize Medication Errors

This article presents an Artificial Intelligence (AI)-based infrastructure to reduce medication errors while following a treatment plan at home. The system, in particular, assists patients who have some cognitive disability. The AI-based system first learns the skills of a patient using the Actor–Critic method. After assessing patients’ disabilities, the system adopts an appropriate method for the monitoring process. Available methods for monitoring the medication process are a Deep Learning (DL)-based classifier, Optical Character Recognition, and the barcode technique. The DL model is a Convolutional Neural Network (CNN) classifier that is able to detect a drug even when shown in different orientations. The second technique is an OCR based on Tesseract library that reads the name of the drug from the box. The third method is a barcode based on Zbar library that identifies the drug from the barcode available on the box. The GUI demonstrates that the system can assist patients in taking the correct drug and prevent medication errors. This integration of three different tools to monitor the medication process shows advantages as it decreases the chance of medication errors and increases the chance of correct detection. This methodology is more useful when a patient has mild cognitive impairment.