Postoperative pain is a prevalent clinical issue that significantly impacts patient recovery, making its management crucial for rehabilitation. Recent studies have shown that postoperative pain not only affects the physiological state of patients but may also disrupt their circadian rhythms, leading to decreased sleep quality and physiological dysfunctions. This review aims to explore the effects of postoperative pain interventions on circadian rhythm disturbances, analyze the underlying mechanisms, and summarize the effective strategies currently used in clinical practice. Through a comprehensive analysis of the relevant literature, we will highlight the importance of pain management during the recovery process and emphasize its potential role in regulating circadian rhythms. Pharmacological treatments like NSAIDs and melatonin have shown efficacy in regulating circadian rhythms and improving sleep quality in postoperative patients. Multimodal analgesia combining pharmacological and non-pharmacological methods (e.g., CBT, acupuncture) can optimize pain relief while minimizing side effects. However, further research is needed to clarify the bidirectional relationship between pain perception and circadian rhythms and translate these findings into clinical practice.

Wearable devices that incorporate artificial intelligence (AI) have revolutionised healthcare through continuous monitoring, early detection, and tailored management of chronic diseases.

This cross-sectional study analysed patients' perceptions, trust, and awareness of AI-driven wearable health technologies, emphasising the identification of primary facilitators and barriers to adoption. A total of 455 participants, comprising individuals with chronic conditions, were recruited through convenience and stratified sampling methods. Data were collected via an online questionnaire that included demographic questions, Likert-scale items, and multiple-choice questions to evaluate awareness of particular AI features and the functionalities of wearable devices.

The findings indicated predominantly positive perceptions, with most participants concurring that wearable devices improve proactive care, facilitate remote consultations, and deliver precise health insights. Concerns regarding technical failures, data accuracy, and the potential reduction of human interaction were significant. No notable demographic differences were identified; however, participants with chronic conditions expressed more favourable perceptions. The research emphasises the necessity of user education, technical reliability, and professional oversight for the successful integration of AI-powered wearables in the management of chronic diseases.

Asthma remains a public health concern in the United States, with mortality disproportionately affecting demographic groups. This study aimed to describe national trends in asthma mortality from 1999 to 2020 and identify demographic and regional disparities.

We retrospectively analyzed mortality data from the CDC WONDER database using International Classification of Diseases, Tenth Revision (ICD-10) codes J45 and J46. Age-adjusted mortality rates (AAMRs) were calculated by sex, race, age group, US Census region, state, and urban-rural classification. Joinpoint regression was employed to detect changes over time.

A total of 82,686 asthma-related deaths were identified (37.2% males, 62.8% females). Overall, the AAMR declined from 1.72 in 1999 to 1.14 in 2020. Joinpoint analysis revealed a significant decline from 1999 to 2009, a plateau from 2009 to 2014, a further decline from 2014 to 2018, and a significant increase from 2018 to 2020. Non-Hispanic Black individuals (AAMR 2.73) and older adults (≥65 years) had the highest mortality rates, with females exhibiting higher rates than males (1.30 vs 0.95).

Despite declining trends, persistent disparities in asthma mortality underscore the need for targeted interventions, improved healthcare access, and ongoing surveillance.

The incidence of chronic diseases is increasing owing to the aging population; in particular, older adults living alone struggle with self-management and medical expenses. Digital health can contribute to medical cost management and health promotion, but its effectiveness for older adults living alone remains unclear. In a rapidly aging society, it is important to demonstrate the effect of digital health on improving the lives of older adults living alone and reducing the burden of chronic diseases.

This study aims to examine the intervention effects of digital health on self-management, quality of life, and medical factors for older adults living alone with common chronic diseases such as cardiovascular disease, respiratory disease, and musculoskeletal disorders through a systematic literature review and meta-analysis.

We searched the literature using 3 databases, including PubMed, CINAHL, and Cochrane CENTRAL, for literature published in overseas academic journals up to October 2024. The final 11 papers were used for analysis based on selection and exclusion criteria. Meta-analysis was used to calculate the mean difference and standardized mean difference (SMD) for the selected literature using RevMan (version 5.4; Cochrane). The effect size and heterogeneity were calculated through 95% CI.

As a result of conducting a meta-analysis of 8 of 11 documents, there was a significant effect of self-management factors on moderate-to-vigorous physical activity (SMD=0.08; z=2.07; P=.04). However, among self-management factors, low-density lipoprotein cholesterol (SMD=-0.04; z=0.91; P=.36) did not show statistically significant results. Among the medical factors, general quality of life (SMD=0.11; z=0.93; P=.35), depression (SMD=-3.95; z=1.59; P=.11), and hospital days (SMD=-1.57; z=0.91; P=.36) also did not show statistically significant results. However, it was confirmed that they improved after a digital health intervention.

This study demonstrated that digital health interventions are effective in improving physical activity in older adults with chronic diseases living alone. However, owing to the characteristics of older adults living alone, there is a need to further expand digital health to combine care services that can manage diseases at home.

Precision management of chronic diseases is crucial for improving patient quality of life and alleviating global health burdens. Advancements at the intersection of medicine and engineering, particularly through artificial intelligence (AI), have driven significant progress in precision care. From the perspective of the full life span management of chronic diseases, we focus on medicine-engineering crossover for monitoring chronic diseases, developing and implementing precision care plans, and evaluating care outcomes. Through an in-depth discussion, we address key issues such as AI's potential to enable precision care and the challenges associated with its implementation, including data accuracy, privacy concerns, and clinical adoption. Emphasizing the importance of nurses embracing new technologies and interdisciplinary collaboration, this paper highlights how technological innovation can improve chronic disease management, particularly by enhancing care efficiency and personalizing health interventions. We aim to support the development of integrated healthcare solutions that improve patient outcomes in chronic disease management.

Failure to rescue (FTR) is defined as mortality within 30 days following a major complication. While FTR has been studied in various brain tumor resections, its predictors in malignant brain tumor resection (mBTR) remain unexplored. This study aims to identify FTR predictors in mBTR resection patients using a frailty-driven model.

Patients undergoing craniotomy for mBTR were identified from the American College of Surgeons-National Surgical Quality Improvement Program database (2012-2020), with frailty measured by the Risk Analysis Index (RAI).

Of 14,721 mBTR patients, 1275 (8.66%) developed major postoperative complications and 166 (13.01%) experienced FTR. The cohort's median age was 59 years (interquartile range: 47-68). Multivariate analysis revealed nonelective surgery (odds ratio [OR]: 1.48, 95% confidence interval [CI]: 1.02-2.16, P < 0.05) as an independent risk factor for FTR. Frailty was a significant independent predictor of FTR with mBTR, with both frail (N = 110) and very frail (N = 22) patients having a 5.34-fold and 8.10-fold higher odds of FTR, respectively (P < 0.001). Expectedly, major postoperative complications were predictive of FTR, including unplanned intubation (OR: 2.56, CI: 1.66-3.95, P < 0.001), prolonged ventilation (OR: 2.00, CI: 1.37-3.14, P < 0.01), cardiac arrest (OR: 16.64, CI: 8.20-33.74, P < 0.001), and septic shock (OR: 2.08, CI: 1.10-3.91, P < 0.05). The RAI-driven frailty model demonstrated excellent discriminatory accuracy for predicting FTR patients undergoing mBTR (c-statistic: 0.82, 95% CI: 0.79-0.85).

Preoperative RAI-measured frailty, alongside nonelective surgery, and major postoperative complications were significant predictors of FTR in mBTR patients. Identifying mBTR patients at risk for FTR using frailty strata may aid in preoperative neurosurgical risk stratification to optimize patients prior to surgery.

The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data. Key advancements, such as techniques improving model efficiency, generalization, and robustness, are examined, showcasing DL's ability to address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous model evaluation are also discussed, underscoring the importance of performance assessment in varied application contexts. The impact of DL in image processing is highlighted through its ability to tackle complex challenges and generate actionable insights. Finally, this survey identifies potential future directions, including the integration of emerging technologies like quantum computing and neuromorphic architectures for enhanced efficiency and federated learning for privacy-preserving training. Additionally, it highlights the potential of combining DL with emerging technologies such as edge computing and explainable artificial intelligence (AI) to address scalability and interpretability challenges. These advancements are positioned to further extend the capabilities and applications of DL, driving innovation in image processing.

In recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. Concurrently, the integration of artificial intelligence with the medical field has garnered increasing attention from medical experts. This study undertakes a dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades to investigate the current status and trends of the fusion between medicine and artificial intelligence.

Following a search on the Web of Science, researchers retrieved all reviews and original articles concerning artificial intelligence in healthcare published between January 1993 and December 2023. The analysis employed Bibliometrix, Biblioshiny, and Microsoft Excel, incorporating the bibliometrix R package for data mining and analysis, and visualized the observed trends in bibliometrics.

A total of 22,950 documents were collected in this study. From 1993 to 2023, there was a discernible upward trajectory in scientific output within bibliometrics. The United States and China emerged as primary contributors to medical artificial intelligence research, with Harvard University leading in publication volume among institutions. Notably, the rapid expansion of emerging topics such as COVID-19 and new drug discovery in recent years is noteworthy. Furthermore, the top five most cited papers in 2023 were all pertinent to the theme of ChatGPT.

This study reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine. Additionally, medical artificial intelligence research is dynamically evolving with the advent of new technologies. Moving forward, concerted efforts to bolster international collaboration and enhance comprehension and utilization of AI technologies are imperative for fostering novel innovations in healthcare.

One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all residents' overall well-being. Thus, this paper proposes an architecture to deliver smart health. The architecture is anchored in the Internet of Things and edge computing, and it is driven by artificial intelligence to establish three foundational layers in smart care. Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in R2, respectively, on training and test sets. Future research works involve extending the performance of the algorithms with new datasets, creating and optimizing embedded AI models, deploying edge-IoT with embedded AI for wearable devices, implementing an autonomous AI cloud engine, and implementing federated learning to deliver scalable smart health in a smart city context.

The burden of food contamination and food wastage has significantly contributed to the increased prevalence of foodborne disease and food insecurity all over the world. Due to this, there is an urgent need to develop a smarter food traceability system. Recent advancements in biosensors that are easy-to-use, rapid yet selective, sensitive, and cost-effective have shown great promise to meet the critical demand for onsite and immediate diagnosis and treatment of food safety and quality control (i.e. point-of-care technology). This review article focuses on the recent development of different biosensors for food safety and quality monitoring. In general, the application of biosensors in agriculture (i.e. pre-harvest stage) for early detection and routine control of plant infections or stress is discussed. Afterward, a more detailed advancement of biosensors in the past five years within the food supply chain (i.e. post-harvest stage) to detect different types of food contaminants and smart food packaging is highlighted. A section that discusses perspectives for the development of biosensors in the future is also mentioned.

Artificial intelligence (AI) is an interdisciplinary field that combines computer technology, mathematics, and several other fields. Recently, with the rapid development of machine learning (ML) and deep learning (DL), significant progress has been made in the field of AI. As one of the fastest-growing branches, DL can effectively extract features from big data and optimize the performance of various tasks. Moreover, with advancements in digital imaging technology, DL has become a key tool for processing high-dimensional medical image data and conducting medical image analysis in clinical applications. With the development of this technology, the diagnosis of orthopedic diseases has undergone significant changes. In this review, we describe recent research progress on DL in fracture diagnosis and discuss the value of DL in this field, providing a reference for better integration and development of DL technology in orthopedics.

The management of chronic diseases has been revolutionized by the advent of wearable health technology. These devices provide personalized and real-time health data to patients. The problem that this technology is most frequently used to address is obesity and its consequences, heart disease, and certain cancers. Numerous studies have shown a correlation between being at a healthy weight and having a lower risk of developing these conditions. Yet, many smart health devices in the wearable technology sector are only, and most of the time, within the context of weight management or obesity. The major research question directing this exploration is as follows: How can the use of wearable technologies be effective in improving the management and quality of life of people with chronic health problems? The question carries with it another that is almost as important: Does the use of these devices move us toward a more moral and just healthcare system, or does it unfairly advantage some groups of patients over others? This research will focus on three specific types of wearable devices, chosen as representative case studies. They are (1) the smartwatch, a more recent advancement in wearable technology that monitors the user's heart rate and physical activity; (2) the continuous glucose monitor (CGM), which presents real-time glucose levels for the user; and (3) the continuous positive airway pressure (CPAP) device, also called a respirator, worn during sleep by individuals diagnosed with sleep apnea. Each of these devices has the potential to not only revolutionize the management of chronic health conditions but also raise some important questions about the ethics of doing so.

The integration of nonfungible tokens (NFTs) in health care, particularly in cardiovascular medicine, represents a disruptive shift toward enhancing the security and interconnection of electronic health data around the patient. NFTs, unique digital certificates stored on a blockchain network, bind various sources of health data to their owner (the patient) and delineate the access rights for stakeholders (providers, researchers) using smart contracts. Data sources might include electronic medical records from different hospitals, clinics, pharmacies, test centres, and mHealth devices. Accordingly, patients and their providers benefit from seamless visibility of diagnoses, medications, electrocardiograms, imaging, home blood pressure logs, and artificial intelligence-enabled insights from these aggregated data. Rather than being stored on proprietary servers, data are encrypted and stored on decentralized networks with a unified point of access and immutable proof of ownership, making them more robust to theft or tampering. As custodians of their NFTs, patients are incentivized to actively partake in their health monitoring and self-driven research that aligns with their needs using innovative marketplaces that allow them to browse studies, document their informed consent, and monetize their contributions. Furthermore, they are empowered to educate themselves and seek care across siloes in traditional settings or virtual platforms such as the metaverse, where NFTs serve as digital passports. Despite these exciting prospects, adoption within the health care sector remains in its infancy, with ethical and technical limitations still being addressed. In this article we explore the multifaceted applications and key players in the field, and outline use-cases for patient-centred cardiovascular care featuring NFTs.

Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist among the public and medical community. Given the rapid and transformative recent growth of AI in cardiovascular care, to inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients' and healthcare providers' perspectives when using AI in cardiovascular care.

In this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI-based medical devices (interventions of interest) in the context of cardiovascular care from patients', caregivers', or healthcare providers' perspectives. The search was completed on May 24, 2022 and was not limited by date or study design.

After reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues (n = 59, 40.7%); risk of healthcare inequity or disparity (n = 36, 24.8%); risk of patient harm (n = 24, 16.6%); accountability and responsibility concerns (n = 19, 13.1%); problematic informed consent and potential loss of patient autonomy (n = 17, 11.7%); and issues related to data ownership (n = 11, 7.6%). Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients' interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights.

This review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients' and healthcare providers' perspectives. Successful integration of AI into cardiovascular care necessitates implementation of mitigation strategies. These strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.

Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.

Existing cuffless blood pressure (BP) monitoring technologies commonly rely on single-site measurements with unimodal sensor configurations, thereby constraining the precision and visualization of the two-dimensional data. Building upon our previous observations regarding the significant influence of measurement sites on BP evaluation, and leveraging the widely acknowledged utility of electrocardiography (ECG) in BP assessment, in this study, we develop a multimodal photo-electro-mechano tonoarteriographic (TAG) imaging system, enabling continuous visualization of local BP variation and estimation of central BP. The system integrates a 3×3 photoplethysmography (PPG) sensor array, one-lead ECG, and a 2×2 pressure sensor array, allowing to collect simultaneously 15 channel physiological signals. The proposed system was tested with 20 subjects and the experimental results reveal noticeable variations in local pulse transit time (PTT)/BP across different anatomical structures at the measurement site. To further improve the system performance, we designed and tested a flexible 2×4 ultrasound sensor array and demonstrated its feasibility to augment the system's capability for central BP estimation. In summary, the proposed system can not only estimate continuously central BP but also visualize the two-dimensional local BP/PTT variation at the measurement site, holding potential to support clinical decision-making and offering geographic-dependent information for micro-circulation investigations.

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Sickle cell anemia (SCA) is a hereditary blood disorder characterized by abnormal hemoglobin, causing red blood cells to assume a sickle shape, leading to various complications. Climate change has emerged as a significant global challenge, influencing environmental conditions worldwide. This paper explores the implications of climatic variations on the prevalence, management, and outcomes of SCA. Climate change affects weather patterns, leading to altered temperatures, increased frequency of extreme weather events, and variations in humidity levels. These changes can have a profound impact on individuals living with SCA. High temperatures exacerbate the symptoms of SCA, potentially triggering painful vaso-occlusive crises due to dehydration and increased blood viscosity. Conversely, cold temperatures may induce vaso-occlusion by causing blood vessels to constrict. Changes in rainfall patterns might also affect water accessibility, which is crucial for maintaining adequate hydration, particularly in regions prone to droughts. The management of SCA is multifaceted, involving regular medical care, hydration, and avoiding triggers that could precipitate a crisis. Adverse weather events and natural disasters can disrupt healthcare infrastructure and access to essential medications and resources for SCA patients, especially in vulnerable communities. To mitigate the implications of climatic change on SCA, interdisciplinary strategies are essential. These strategies may include enhancing healthcare systems' resilience to climate-related disruptions, implementing adaptive measures to address changing environmental conditions, and promoting public awareness and education on managing SCA amidst climate variability. In conclusion, climatic variations pose significant challenges for individuals with SCA, affecting the prevalence, management, and outcomes of the disease.

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide. The advent of smart technologies has significantly impacted the management of CVD, offering innovative tools and solutions to improve patient outcomes. Smart technologies have revolutionized and transformed the management of CVD, providing innovative tools to improve patient care, enhance diagnostics, and enable more personalized treatment approaches. These smart tools encompass a wide range of technologies, including wearable devices, mobile applications,3D printing technologies, artificial intelligence (AI), remote monitoring systems, and electronic health records (EHR). They offer numerous advantages, such as real-time monitoring, early detection of abnormalities, remote patient management, and data-driven decision-making. However, they also come with certain limitations and challenges, including data privacy concerns, technical issues, and the need for regulatory frameworks. In this review, despite these challenges, the future of smart technologies in CVD management looks promising, with advancements in AI algorithms, telemedicine platforms, and bio fabrication techniques opening new possibilities for personalized and efficient care. In this article, we also explore the role of smart technologies in CVD management, their advantages and disadvantages, limitations, current applications, and their smart future.

Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications.

We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements.

This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions.

Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%.

Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.

This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

Mobile health (mHealth) services enable real-time measurement of information on individuals' biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted.

The study aims to identify the predictors of older people's intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions.

A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method.

Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (β=.453; P=.003) and social influence (β=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (β=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=-2.165). Bootstrapping also confirmed that device trust (β=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease.

This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users.

Smart Engineering (SE) describes the methods, processes, and IT tools for the interdisciplinary, system-oriented development of innovative, intelligent, networked products, production plants, and infrastructures [...].

Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.

Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry.

Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages.

The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

This study aimed to evaluate a clothing prototype that incorporates sensors for the evaluation of pressure, temperature, and humidity for the prevention of pressure injuries, namely regarding physical and comfort requirements. A mixed-method approach was used with concurrent quantitative and qualitative data triangulation. A structured questionnaire was applied before a focus group of experts to evaluate the sensor prototypes. Data were analyzed using descriptive and inferential statistics and the discourse of the collective subject, followed by method integration and meta-inferences. Nine nurses, experts in this topic, aged 32.66 ± 6.28 years and with a time of profession of 10.88 ± 6.19 years, participated in the study. Prototype A presented low evaluation in stiffness (1.56 ± 1.01) and roughness (2.11 ± 1.17). Prototype B showed smaller values in dimension (2.77 ± 0.83) and stiffness (3.00 ± 1.22). Embroidery was assessed as inadequate in terms of stiffness (1.88 ± 1.05) and roughness (2.44 ± 1.01). The results from the questionnaires and focus groups' show low adequacy as to stiffness, roughness, and comfort. The participants highlighted the need for improvements regarding stiffness and comfort, suggesting new proposals for the development of sensors for clothing. The main conclusions are that Prototype A presented the lowest average scores relative to rigidity (1.56 ± 1.01), considered inadequate. This dimension of Prototype B was evaluated as slightly adequate (2.77 ± 0.83). The rigidity (1.88 ± 1.05) of Prototype A + B + embroidery was evaluated as inadequate. The prototype revealed clothing sensors with low adequacy regarding the physical requirements, such as stiffness or roughness. Improvements are needed regarding the stiffness and roughness for the safety and comfort characteristics of the device evaluated.

The ECHO (Extensions Community Healthcare Outcomes) model of healthcare delivery has grown rapidly since its establishment and increased in popularity in recent years. This expansion has developed alongside the growing incidence of chronic diseases and the need to better manage them. The increasing uptake in ECHO has presented a requirement to assess its true value as healthcare costs are increasing globally, resulting in a growing demand by governments and policy makers to ensure chronic disease management strategies provide true value. Therefore, the aim of this review is to examine the impact that ECHO has on clinical practice and how such impacts are measured or evaluated. A narrative literature review is carried out to examine the outcomes assessed in ECHO-related studies. Three key academic databases were utilised for the literature search: Web of Science, PubMed, and Medline. Keywords relating to the review were chosen and searched for. Papers were screened using specified inclusion and exclusion criteria relating to years of publication (2000-2020), type of publication (original research, review papers and meta-analyses) and language requirements (English language only). This review found that while the ECHO model is expanding, and improving the so-called "knowledge gap" between specialists and primary care physicians, there is also a gap in the ways value is examined within ECHO. Most studies on ECHO lack an examination of patient reported health outcomes and appropriate, comparative costing methods. Current ECHO-related studies lack vital components that demonstrate the value of the model. Such components include patient reported health outcomes and detailed costing comparisons between the ECHO model and the traditional care pathway it is replacing.

Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor's environment, the patient's environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.

Chronic disease is the leading cause of morbidity and mortality on a global scale. Recent use of intelligent home environments for remote health monitoring has allowed patients to feel safe at home. By encouraging patient engagement and, if necessary, care delivery, smart home environments may be a useful method for managing chronic diseases at home. The purpose of this study is to synthesize the evidence on the usage of smart healthcare in the home for chronic illness management.

We conducted a scoping review using the Joanna Briggs Methodology and searched three databases from 2017 to 2022 for original research papers on smart healthcare, smart home technology, home-based technology, home monitoring, and physiological monitoring for chronic illness management. We did a descriptive study on the pertinent data we collected, as well as an analysis of whether the devices met its objectives.

The final analysis included nine papers, the majority of which were randomized controlled trials. All of the studies were carried out in developed countries. The gadgets or smart healthcare in these studies are categorized based on the technology used and the outcomes measured. Respiratory, weight, and ballistocardiograph measurements, as well as changes in questionnaire ratings, hospitalization, activity monitoring, device acceptability, medication adherence, exercise capacity, and body function, were all measured.

Smart healthcare applications boost health by monitoring health and wellness, recording physical activity and rehabilitation, and improving overall quality of life. Not all smart home applications, however, served their intended purpose. As a result, more research into the efficacy of smart healthcare is needed to improve its application for chronic illness treatment.

The need for innovation in the healthcare sector is essential to meet the demand of a rapidly growing population and the advent of progressive chronic ailments. Over the last decade, real-time monitoring of health conditions has been prioritized for accurate clinical diagnosis and access to accelerated treatment options. Therefore, the demand for wearable biosensing modules for preventive and monitoring purposes has been increasing over the last decade. Application of machine learning, big data analysis, neural networks, and artificial intelligence for precision and various power-saving approaches are used to increase the reliability and acceptance of smart wearables. However, user compliance and ergonomics are key areas that need focus to make the wearables mainstream. Much can be achieved through the incorporation of smart materials and soft electronics. Though skin-friendly wearable devices have been highlighted recently for their multifunctional abilities, a detailed discussion on the integration of smart materials for higher user compliance is still missing. In this review, we have discussed the principles and applications of sustainable smart material sensors and soft electronics for better ergonomics and increased user compliance in various healthcare devices. Moreover, the importance of nanomaterials and nanotechnology is discussed in the development of smart wearables.

The increasing of chronic diseases and related health management are the main clinical and public health challenges. The long-term nature and the need for continuous monitoring in chronic disease management gave rise to early technological innovations (mobile Health) to improve care management plans, therapeutic adherence, and psychological support to the patient. This review aimed to map the literature on the impact of the use of wearable device on quality of life in patients with chronic diseases. We performed a systematic search of MEDLINE through PubMed, Web of Science, and Scopus of all scientific literature published until January 2022. Based on inclusion and exclusion criteria, a total of 10 papers were included. This review pointed out the relevant focus on the use of wearable device in chronic disease patients highlighting the wearable device impact on several domains including quality of life, Self-Efficacy, Self-Management, and feelings on patients with chronic diseases. The available scientific literature related to the impact of the use of wearable device on quality of life and psychological features in patients with chronic diseases, general underline a need to develop professional healthcare guidelines and tailored intervention on patients with a chronic condition, using mobile Health solutions and trying to fill the lack of knowledge about the topic.

Modern and innovative technologies are rapidly penetrating the clinical practices of Orthopaedic Surgeons. The ones that have proved successful for clinical use are Additive Manufacturing/3D printing, Artificial Intelligence, Robotics, Smart sensors, and Orthobiologics. Industry 5.0 revolution has helped provide personalised treatment by integrating machines and human beings. In this special issue, we present a collection of excellent articles on these technologies.

It is essential to change health services from a hospital to a patient-centric platform since medical costs are steadily growing and new illnesses are emerging on a worldwide scale. This study provides an optimal decision support system based on the cloud and Internet of Things (IoT) for identifying Chronic Kidney Disease (CKD) to provide patients with efficient remote healthcare services. To identify the presence of medical data for CKD, the proposed technique uses an algorithm named Improved Simulated Annealing-Root Mean Square -Logistic Regression (ISA-RMS-LR). The four subprocesses that make up the proposed model are a collection of data, preprocessing, feature selection, and classification. The incorporation of Simulated Annealing (SA) during Feature Selection (FS) enhances the ISA-RMS-LR model's classifier outputs. Using the CKD benchmark dataset, the ISA-RMS-LR model's efficacy has been verified. According to the experimental findings, the proposed ISA-RMS-LR model effectively classifies patients with CKD, with high sensitivity at 99.46%, accuracy at 99.26%, Specificity at 98%, F-score at 99.63%, and kappa value at 98.29%. The proposed system has many benefits including the fast transmission of medical data to the medical personnel, real-time tracking, and registration condition of the patient through a medical record. Potential enhancement of the performance measures the provider system's hospital capacity and monitoring of a significant number of patients with a concentrated average delay.

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients' biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient's medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient's health.