Improve Patient Monitoring Research Articles

Transforming Patient Outcomes: Cutting-Edge Applications of AI and ML in Predictive Healthcare

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has emerged as a transformative force, offering unprecedented potential to enhance patient outcomes. This paper explores the cutting-edge applications of AI and ML in predictive healthcare, focusing on how these technologies are revolutionizing early detection, personalized treatment, and patient management. Through advanced algorithms and data-driven insights, AI and ML are enabling healthcare providers to predict disease progression, optimize therapeutic interventions, and improve patient monitoring in real-time. Key applications discussed include predictive modeling for chronic disease management, AI-powered diagnostic tools, and the use of machine learning in precision medicine. The paper also addresses the challenges associated with these technologies, including data privacy concerns, algorithmic bias, and the need for robust validation. By examining current case studies and future trends, this work aims to highlight the transformative impact of AI and ML on patient care, ultimately driving a shift toward more proactive, tailored, and efficient healthcare systems.

The role of machine learning in the improvement of physician-nurse relationships when the management of burn patients: A narrative review

In recent years, machine learning (ML) has emerged as a transformative technology in healthcare, providing significant advancements in patient care and management. Burn care, which necessitates comprehensive and coordinated efforts due to the severe and multifaceted nature of burn injuries, particularly benefits from ML's capabilities. This literature review investigates how ML enhances the collaboration between physicians and nurses in managing burn patients. In the present study, significant findings show that ML's predictive analytics can predict patient outcomes and complications, helping with proactive care strategies. ML-driven decision support systems offer real-time, evidence-based recommendations, ensuring consistent care approaches. In addition, ML-powered virtual simulations improve training and comprehension of roles, while advanced electronic health records (EHR) systems streamline documentation and information sharing. Continuous quality improvement is supported by ML's data-driven insights, leading to improved patient monitoring and management. Ultimately, integrating ML in burn care significantly improves physician-nurse collaboration, resulting in better patient outcomes. This includes reduced infection rates and shorter hospital stays. This highlights the vital role of ML in transforming healthcare delivery and professional collaboration in managing complex conditions like burn injuries.

Complex interplays: Asthma management and maternal‑fetal outcomes in pregnancy (Review).

Asthma, a common chronic respiratory condition, poses unique challenges in pregnancy, impacting both maternal and fetal health. Of note, 8-13% of pregnant women suffer from asthma, a condition that can worsen, stabilize, or improve during pregnancy. These fluctuations necessitate a nuanced management strategy to ensure the health of both the mother and fetus. Adverse outcomes, such as preeclampsia, gestational diabetes and increased cesarean delivery rates are associated with poorly controlled asthma. From a fetal perspective, the risks include preterm birth and a low birth weight. Physiological changes in pregnancy, such as an increased tidal volume and altered drug metabolism due to increased blood volume, complicate the management of asthma. The safety of asthma medications during pregnancy remains a significant concern, with ongoing research into their teratogenic effects. Recent advancements in treatment include the development of biologics and the increased use of personalized medicine, integrating pharmacogenomics and immunological profiling to tailor treatments to individual needs. Digital health tools have also emerged, enabling improved patient monitoring and management. The present review highlights the complex interplay between asthma management and pregnancy outcomes, advocating for comprehensive care approaches that consider the dynamic physiological changes during pregnancy. It underscores the need for ongoing research into the safety of medication and innovative therapeutic strategies to improve health outcomes for pregnant women with asthma and their babies.

Transforming in-clinic post-operative and intermediate care with cosinuss°

Continuous, mobile patient monitoring plays a critical role in healthcare, particularly for post-surgery, intermediate care in clinics. The implementation of vital signs monitoring technology enables healthcare professionals to triage patients effectively by maintaining real-time awareness of their health status and allowing for prompt intervention when necessary. This technology supports early mobilization and facilitates the detection of potential complications such as post-surgical sepsis. cosinuss° technology has been evaluated in various studies, in terms of its accuracy in capturing vital parameters and its usability, emphasizing its potential to enhance intermediate patient care and outcomes. This report outlines the design and implementation of cosinuss° Health patient monitoring solution for use in intermediate, postoperative clinic settings. It presents the results and insights from three recent, in-clinic applications, discussing both technical and practical aspects, clinical processes, and the reported satisfaction from both patients and medical caregivers. The findings highlight the promising potential of cosinuss° Health on improving patient monitoring and overall clinical outcomes.

Enhancing flexibility of smart bioresorbable vascular scaffolds through 3D printing using polycaprolactone and polylactic acid

With the growing use of stent implantations, the risk of in-stent restenosis represents a major concern for cardiovascular patients. Herein, we propose a hybrid bioresorbable vascular scaffold (H-BVS) integrated with a wireless sensor that could enable significant advances in cardiovascular therapeutics. The H-BVS was carefully manufactured using a state-of-the-art customized 3D printer equipped with dual nozzles to achieve a synergistic combination of polycaprolactone (PCL) and polylactic acid (PLA), both of which are known for their bioresorbable properties. This characteristic is particularly valuable in medical applications where the scaffold is gradually absorbed by the body, eliminating the need for a second procedure to remove it. Furthermore, the H-BVS was integrated with a wireless, battery-less LC-type pressure sensor for real-time monitoring of in-stent restenosis. This wireless sensor was meticulously connected with the H-BVS framework through engineered microstructures, which not only ensured a robust bond but also significantly enhanced the overall integrity and functionality of the device. Additionally, the combination of the H-BVS and sensor was optimized to exhibit superior bending flexibility and radial strength, which are key factors in ensuring effectiveness and patient safety. The feasibility of the smart H-BVS (SH-BVS) was verified through comprehensive testing within a phantom system designed to simulate real blood flow conditions. We believe that the SH-BVS system holds strong potential to significantly improve patient monitoring and treatment strategies within the cardiovascular healthcare domain.

Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility.

Maternal health remains a critical global health challenge, with disparities in access to care and quality of services contributing to high maternal mortality and morbidity rates. Artificial intelligence (AI) has emerged as a promising tool for addressing these challenges by enhancing diagnostic accuracy, improving patient monitoring, and expanding access to care. This review explores the transformative role of AI in maternal healthcare, focusing on its applications in the early detection of pregnancy complications, personalized care, and remote monitoring through AI-driven technologies. AI tools such as predictive analytics and machine learning can help identify at-risk pregnancies and guide timely interventions, reducing preventable maternal and neonatal complications. Additionally, AI-enabled telemedicine and virtual assistants are bridging healthcare gaps, particularly in underserved and rural areas, improving accessibility for women who might otherwise face barriers to quality maternal care. Despite the potential benefits, challenges such as data privacy, algorithmic bias, and the need for human oversight must be carefully addressed. The review also discusses future research directions, including expanding AI applications in maternal health globally and the need for ethical frameworks to guide its integration. AI holds the potential to revolutionize maternal healthcare by enhancing both care quality and accessibility, offering a pathway to safer, more equitable maternal outcomes.

Portable Sensing Probe for Real-Time Quantification of Ammonia in Blood Samples.

The detection of ammonia levels in blood is critical for diagnosing and monitoring various medical conditions, including liver dysfunction and metabolic disorders. However, traditional diagnostic methods are slow and cumbersome, often involving multiple contact-based steps such as ammonia separation in alkali conditions followed by distillation or microdiffusion, leading to delays in diagnosis and treatment. Herein, we developed a colorimetric assay capable of rapid detection of ammonia in whole blood or plasma samples, utilizing 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO)-oxidized cellulose nanocrystals (TCNC) coupled with gold nanoparticles (AuNPs). The basis of our assay relies on either (i) the interaction between the carboxylate group (-COO) of TEMPO and ammonium ions or (ii) the manipulation of AuNPs surface plasmon resonance (SPR) through the formation of Au(NH3)43+, which displaces a redox mediator, resazurin, resulting in observable multicolor displays at various concentrations of ammonia. The colorimetric assay exhibits a wide linear detection range for dissolved NH4+ (0.1-37 μM) with a low limit of detection (LOD) of 0.1 μM. Additionally, it effectively measures NH3(g) concentrations in the range of 0.5-144 μM. The fabricated electrochemical nose (E-nose) device demonstrates excellent analytical performance for plasma ammonia sensing (0.05-256 μM). Experimental results demonstrate a linear detection range suitable for clinical applications, with excellent correlation to standard laboratory methods, offering a practical solution for point-of-care (PoC) testing. We anticipate that this approach can be applied broadly to improve patient monitoring and treatment by providing immediate and accurate ammonia measurements in a clinical setting.

IoT-AI in Healthcare: A Comprehensive Survey of Current Applications and Innovations

The convergence of IoT and AI technology has the capacity to revolutionize healthcare by facilitating the gathering of real-time data and employing sophisticated analytics for tailored medical solutions. This survey provides an in-depth examination of IoT-AI applications in healthcare, specifically focusing on wearable devices such as smart bands and wristbands, as well as health monitoring systems. We present the core principles of IoT and AI, examining their synergistic integration in healthcare environments. The taxonomy of IoT-AI-based healthcare systems is comprehensive and classifies them according to their architectural components, data processing algorithms, and application domains. The survey showcases distinctive achievements, including novel methodologies for combining data and making predictions, frameworks for improving patient monitoring, and inventive methods for delivering healthcare remotely. We offer a comprehensive examination of key challenges such as data privacy, interoperability, and regulatory compliance, and analyze their specific effects on the implementation and efficacy of IoT-AI healthcare systems. The comparison analysis encompasses measures such as system performance, accuracy, and user satisfaction, providing valuable insights into the strengths and limitations of different techniques. In addition, we analyze developing patterns and clearly outline future areas of study, such as the enhancement of stronger security protocols, the use of blockchain technology to ensure data integrity, and the progress in AI algorithms to achieve more precise diagnoses. Emerging trends such as Digital Twins and SLUC are identified as promising avenues for future research. In conclusion, this study provides a detailed framework that enhances the understanding of IoT-AI healthcare systems and offers practical insights for improving healthcare practices and guiding technology adoption.

Combined Metagenomic Viral Detection and Donor-Derived Cell-Free DNA Quantification in Plasma From Kidney Transplant Recipients

BackgroundKidney transplant recipients require potent immunosuppression and are predisposed to opportunistic infections, many of which have a viral etiology. Currently, viral assays detect and quantify single pathogens using PCR or qPCR. An unbiased sequencing method with comparable accuracy would allow simultaneous monitoring of multiple viral pathogens and nonpathogenic Anelloviridae. The quantification of donor-derived cell-free DNA (dd-cfDNA) is an established method for the detection of allograft rejection, and a single workflow combining dd-cfDNA quantification and viral detection represents an opportunity to improve patient monitoring and management. MethodsWhole genome sequencing of cell-free DNA was performed using 1,980 plasma samples from 256 subjects enrolled in a multi-center study. Non-human sequences underwent reference-assisted assembly and taxonomic annotation of the viral DNA pathogens. ResultsOf the 1,980 samples tested, 1,453 (73.4%) had ≥1 viral detection(s), either a known viral pathogen or torque teno virus (TTV), with positivity rates generally declining 12–18 months post-transplant. Concordance of metagenomic NGS (mNGS) viral detection with qPCR detection was 97.7% (94.1% sensitivity, 98.2% specificity), and a linear relationship was demonstrated between mNGS viral quantitation and qPCR results. BK virus, cytomegalovirus, and Epstein-Barr virus were detected by sequencing up to 60 days prior to independently established clinical diagnoses. ConclusionsWhole-genome sequencing allows simultaneous quantification of dd-cfDNA as well as sensitive and early detection of viral infection through secondary analysis of the same sequencing results. In combination with dd-cfDNA, mNGS viral detection may provide additional pathogen surveillance results and serve as a useful biomarker for both over- and under-immunosuppression.

A Hybrid Localization Algorithm for Enhanced Accuracy and Robustness in Healthcare Systems

This paper presents a novel hybrid localization algorithm designed for healthcare systems, integrating Received Signal Strength Indicator (RSSI) and Time of Arrival (ToA) measurements with machine learning techniques. The algorithm aims to enhance the accuracy, robustness, and computational efficiency of sensor localization in dynamic healthcare environments. Experimental results demonstrate that the hybrid algorithm achieves a significantly lower localization error, averaging 0.5 meters, compared to traditional RSSI-only and ToA-only methods. The algorithm's rapid convergence and low computational time make it suitable for real-time applications. Additionally, its robustness to measurement noise, a common challenge in healthcare settings, underscores its reliability. This research underscores the potential of advanced localization technologies to improve patient monitoring, safety, and overall healthcare delivery, with future work poised to further enhance performance and adaptability.

Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies

The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making.

Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care

The primary objective of this commentary was to identify the strengths and weaknesses of AI technologies, uncover opportunities for improvement, and recognize potential threats that could impede their successful implementation in nursing care. This commentary involved constructing a SWOT matrix to analyze AI adoption, identifying internal strengths and weaknesses, and external opportunities and threats. The analysis revealed several strengths of AI adoption in nursing care, including enhanced data analysis capabilities, improved patient monitoring, and increased efficiency in routine tasks. However, weaknesses such as the high initial costs of implementation and concerns about data security were identified. Opportunities included the potential for AI to reduce healthcare costs and improve patient outcomes. Nonetheless, threats such as resistance to technological change and ethical dilemmas related to AI decision-making processes were recognized as potential barriers to successful adoption. This article sheds light on the intricate landscape of AI adoption in nursing care. While AI brings forth substantial strengths, it simultaneously poses challenges that healthcare systems should confront. To fully harness AI's potential, healthcare organizations should thoughtfully deliberate on the identified weaknesses and threats, actively seeking avenues for seamless integration. In this concerted effort, the healthcare industry is poised to unlock the transformative capabilities of AI, elevating nursing care standards, and ultimately, advancing patient outcomes.

Visibility-based layout of a hospital unit - An optimization approach.

Apatient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. Themedical literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. To fill this gap, we adopt an interdisciplinary approach that combines the human field of view with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient's bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-shaped, I-shaped, and Radial; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visible target in a room when viewed by the nurse from the nursing station. The algorithm considers nurses' horizontal visual field and their depth of vision. Owing to the difficulty in solving the bi-objective model, we also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions. Our findings suggest that the Radial layout appears to outperform the other two layouts in terms of the visibility-based objectives. We found that with a Radial layout, there can be an improvement of up to 50% in equity measure compared to an I-shaped layout. Similar improvements were observed when compared to the L-shaped layout as well. Further, the position of the patient's bed plays a role in maximizing the visibility of the patient's room.Insights from our work will enable understanding and quantifying the relationship between a physical layout and the corresponding provider-to-patient visibility to reduce adverse events.

Artificial Intelligence and Machine Learning Techniques for Anomaly Detection and Threat Mitigation in Cloud-Connected Medical Devices

The Internet of Medical Things (IoMT) has begun functioning like this: improved patient monitoring and an easily accessible digital data warehouse. Despite that, this methodology of the internet will potentially have a counter balance which risks for patient data might involve hacking, data theft, and unauthorized access that may contain great consequences for patient privacy and safety. This article examines the possibility of utilizing new AI technology, including inter alia deep learning, unsupervised learning, and ensemble learning to further boost anomaly detection and threat management in connected cloud medical systems. Many old rules and approaches based on statistics lose relevancy versus the dynamics and unpredictability of modern cyberattacks. Identification of anomalies in cyber security is nearly unavoidable, and it should be the first and the last reaction for detecting irregularities in behavior that may indicate undesirable acts or attacks. The paper aims at understanding how AI/ML approaches can give more sophisticated and versatile interventions for finding out anomalies in cloud-attached medical machines. Moreover, this research details robust AI/ML methods such as the adversarial machine learning and reinforcement learning for a perfect threat mitigation. These techniques which activates machine learning models to learn from data continuing to adjust to new evolving threats and then to establish intelligent and proactive threat response systems. The data experiment, which focuses on relevant data sets, reveals that it is the AI/ML techniques that possess the upper hand over traditional methods when it comes to identifying anomalies and defending against threats for cloud- connected medical devices. Such finding expresses much significance for the healthcare industry, as it gives room for the inclusion of AI/ML techniques into the security systems of the medical devices, which are all connected to the cloud. Through the employment of these strategies, healthcare units will become better able to detect and halt any form of threat and as a consequence patients’ data will be protected, devices will continue operating effectively, and eventually patients’ safety and healthcare units will benefit and gain trust from patients.

Artificial Intelligence and Machine Learning Techniques for Anomaly Detection and Threat Mitigation in Cloud-Connected Medical Devices

The Internet of Medical Things (IoMT) has begun functioning like this: improved patient monitoring and an easily accessible digital data warehouse. Despite that, this methodology of the internet will potentially have a counter balance which risks for patient data might involve hacking, data theft, and unauthorized access that may contain great consequences for patient privacy and safety. This article examines the possibility of utilizing new AI technology, including inter alia deep learning, unsupervised learning, and ensemble learning to further boost anomaly detection and threat management in connected cloud medical systems. Many old rules and approaches based on statistics lose relevancy versus the dynamics and unpredictability of modern cyberattacks. Identification of anomalies in cyber security is nearly unavoidable, and it should be the first and the last reaction for detecting irregularities in behavior that may indicate undesirable acts or attacks. The paper aims at understanding how AI/ML approaches can give more sophisticated and versatile interventions for finding out anomalies in cloud-attached medical machines. Moreover, this research details robust AI/ML methods such as the adversarial machine learning and reinforcement learning for a perfect threat mitigation. These techniques which activates machine learning models to learn from data continuing to adjust to new evolving threats and then to establish intelligent and proactive threat response systems. The data experiment, which focuses on relevant data sets, reveals that it is the AI/ML techniques that possess the upper hand over traditional methods when it comes to identifying anomalies and defending against threats for cloud-connected medical devices. Such finding expresses much significance for the healthcare industry, as it gives room for the inclusion of AI/ML techniques into the security systems of the medical devices, which are all connected to the cloud. Through the employment of these strategies, healthcare units will become better able to detect and halt any form of threat and as a consequence patients’ data will be protected, devices will continue operating effectively, and eventually patients’ safety and healthcare units will benefit and gain trust from patients.

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

The effect of visitation restrictions on ED error.

EDs restricted visitors during the COVID-19 pandemic on the assumption that the risks of disease spread outweighed the psychological benefits of liberal visitation. But data suggest that beyond providing emotional support, family and caregivers can clarify history, improve patient monitoring, and advocate for patients-actions that can improve quality of care. Our objective was to assess whether removing visitors from the bedside contributed to errors in emergency care. We reviewed a database of medical errors covering visits from 11/15/17 to 7/30/22 at an urban, tertiary-care, academic ED for five types of error amenable to visitor intervention: inadequate history gathering, inadequate monitoring, falls, giving a medication to which a patient is allergic, and inappropriate medication dosing. These records were reviewed by two investigators to determine the likelihood visitor presence could have prevented the error. For those errors judged susceptible to visitor intercession, the number in each category was compared for the period before and after strict restrictions took effect. Our review found 27/781 (3.5%) errors in the pre-pandemic period and 27/568 (4.8%) errors in the pandemic period fell into one of these five categories (p = 0.29). Visitors prevented harm from reaching the patient in three of 27 pre-pandemic errors (11.1%), compared to 0 out of 27 peri-pandemic errors (p = 0.23). On review by two attendings, 17/24 (70.8%) errors that reached the patient in the pre-pandemic period were judged amenable to visitor intervention, compared to 25/27 (92.6%) in the pandemic period (p = 0.09). There were no statistically significant differences in the categories of error between the two groups; monitoring errors came the closest: 1/17 (5.9%) pre-COVID errors amenable to visitor intervention in these categories were monitoring related, whereas 7/25 (28.0%) post-COVID errors were (p = 0.16). While this study did not demonstrate a statistically significant difference in error between lenient and restrictive visitation eras, we did find multiple cases in the pre-COVID era in which family presence prevented error, and qualitative review of post-COVID errors suggested many could have been prevented by family presence. Larger trials are needed to determine how frequent and consequential such errors are and how to balance the public health imperative of curbing disease spread with the harm caused by restricting visitation.

Bioinspired colloidal crystal hydrogel pressure sensors with Janus wettability for uterus cervical canal tension perception.

The pursuit of flexible, sensitive, and cost-effective pressure sensors plays a pivotal role in medical diagnostics, particularly in the domain of cervical health monitoring. However, significant challenges remain in the economical production of flexible piezoresistive materials and the integration of microstructures aimed at enhancing sensor sensitivity. This urge highlights the use of innovative, stable hydrogel films that demonstrate robust adherence to soft biological tissues, thereby enabling prolonged bio-signal monitoring. In this study, we introduce an innovative integration of a flexible pressure electrical signal sensor with structural color hydrogel scaffolds. This integration leverages the tunability of the inverse opal structure to fine-tune the scaffold's adherence to the endocervical wall under varying environmental conditions and to amplify the sensitivity of pressure measurements. Our findings indicate that this novel approach holds promise for substantial enhancements in the manufacturing and functional capabilities of cervical pressure sensors, potentially revolutionizing personalized medical treatments and improving patient monitoring.

A sustainable artificial-intelligence-augmented digital care pathway for epilepsy: Automating seizure tracking based on electroencephalogram data using artificial intelligence.

Scalp electroencephalograms (EEGs) are critical for neurological evaluations, particularly in epilepsy, yet they demand specialized expertise that is often lacking in many regions. Artificial intelligence (AI) offers potential solutions to this gap. While existing AI models address certain aspects of EEG analysis, a fully automated system for routine EEG interpretation is required for effective epilepsy management and healthcare professionals' decision-making. This study aims to develop an AI-augmented model for automating EEG seizure tracking, thereby supporting a sustainable digital care pathway for epilepsy (DCPE). The goal is to improve patient monitoring, facilitate collaborative decision-making, ensure timely medication adherence, and promote patient compliance. The study proposes an AI-augmented framework using machine learning, focusing on quantitative analysis of EEG data to automate DCPE. A focus group discussion was conducted with healthcare professionals to find the problem of the current digital care pathway and assess the feasibility, usability, and sustainability of the AI-augmented system in the digital care pathway. The study found that a combination of random forest with principal component analysis and support vector machines with KBest feature selection achieved high accuracy rates of 96.52% and 95.28%, respectively. Additionally, the convolutional neural networks model outperformed other deep learning algorithms with an accuracy of 97.65%. The focus group discussion revealed that automating the diagnostic process in digital care pathway could reduce the time needed to diagnose epilepsy. However, the sustainability of the AI-integrated framework depends on factors such as technological infrastructure, skilled personnel, training programs, patient digital literacy, financial resources, and regulatory compliance. The proposed AI-augmented system could enhance epilepsy management by optimizing seizure tracking accuracy, improving monitoring and timely interventions, facilitating collaborative decision-making, and promoting patient-centered care, thereby making the digital care pathway more sustainable.

A secure blockchain framework for the internet of medical things

AbstractTechnological advancements in the Internet of Medical Things (IoMT) enable medical services to provide incredibly challenging real‐time functions. The proliferation of health sensors has improved patient monitoring, both for patient care and medical purposes, giving professionals more data for early diagnosis and treatment recommendations. Privacy and security are critical considerations for collecting and transferring data from these heterogeneous sensors across the database and the IoT network. This study leverages blockchain technology to improve Electronic Health Record (EHR) privacy and security. This article proposes a novel decentralized architecture using IoT and Blockchain technology for storing EHR and monitoring real‐time patient data. The proposed patient‐centric framework and architecture offer a straightforward solution that fulfills data integrity and anonymity and meets the data interoperability demands. Self‐contained encryption–decryption and primitive cryptographic techniques enhance the system's security. The proposed study models IoT health sensors such as body temperature, pulse rate, spO2, and Electromyogram (EMG) in a blockchain‐based IoT framework. The proposed system has been evaluated to determine that it can process and store Remote Patient Monitoring (RPM) data with encouraging results. The article provides performance, security, and threat analysis to contribute toward a robust, secure, and smart healthcare system using metrics such as latency, throughput, IoT sensor activity, and resource usage. Experimental results present that the proposed method beats the standard healthcare system regarding service quality and patient data monitoring.