Wearable and Mobile Sensors for Personalized Nutrition

An antifouling electrochemical biosensor based on oxidized bacterial cellulose and quaternized chitosan for reliable detection of involucrin in wound exudate

The monitoring of biomarkers in wound exudate is of great importance for wound care and treatment, and electrochemical biosensors with high sensitivity are potentially useful for this purpose. However, conventional electrochemical biosensors always suffer from severe biofouling when performed in the complex wound exudate. Herein, an antifouling electrochemical biosensor for the detection of involucrin in wound exudate was developed based on a wound dressing, oxidized bacterial cellulose (OxBC) and quaternized chitosan (QCS) composite hydrogel. The OxBC/QCS hydrogel was prepared using an in-situ chemical oxidation and physical blending method, and the proportion of OxBC and QCS was optimized to achieve electrical neutrality and enhanced hydrophilicity, therefore endowing the hydrogel with exceptional antifouling and antimicrobial properties. The involucrin antibody SY5 was covalently bound to the OxBC/QCS hydrogel to construct the biosensor, and it demonstrated a low limit of detection down to 0.45 pg mL-1 and a linear detection range from 1.0 pg mL-1 to 1.0 μg mL-1, and it was capable of detecting targets in wound exudate. Crucially, the unique antifouling and antimicrobial capability of the OxBC/QCS hydrogel not only extends its effective lifespan but also guarantees the sensing performance of the biosensor. The successful application of this wound dressing, OxBC/QCS hydrogel for involucrin detection in wound exudate demonstrates its promising potential in wound healing monitoring.

AI nutritionist: Intelligent software as the next generation pioneer of precision nutrition

With the rapid development of information technology and artificial intelligence (AI), people have acquired the abilities and are encouraged to develop intelligent tools and software, which begins to shed light on intelligent and precise food nutrition. Despite the rapid development of such software, disparities still exist in terms of methodology, contents, and implementation strategies. Hence, a set of panoramic profiles is urgently needed to elucidate their values and guide their future development. Here a comprehensive review was conducted aiming to summarize and compare the objects, contents, intelligent algorithms, and functions realized by the already released software in current research. Consequently, 177 AI nutritionists in recent years were collected and analyzed. The advantages, limitations, and trends concerning their application scenarios were analyzed. It was found that AI nutritionists have been gradually advancing the production modes and efficiency of food recognition, dietary recording/monitoring, nutritional assessment, and nutrient/recipe recommendation. Most AI nutritionists have a relatively low level of intelligence. However, new trends combining advanced AI algorithms, intelligent sensors and big data are coming with new applications in real-time and precision nutrition. AI models concerning molecular-level behaviors are becoming the new focus to drive AI nutritionists. Multi-center and multi-level studies have also gradually been realized to be necessary.

Ultra-Sensitive and Stable Multiplexed Biosensors Array in Fully Printed and Integrated Platforms for Reliable Perspiration Analysis

Electrochemical biosensors have emerged as one of the promising tools for tracking human body physiological dynamics via non-invasive perspiration analysis. However, it remains a key challenge to integrate multiplexed sensors in a highly controllable and reproducible manner to achieve long-term reliable biosensing, especially on flexible platforms. Herein, a fully inkjet printed and integrated multiplexed biosensing patch with remarkably high stability and sensitivity is reported for the first time. These desirable characteristics are enabled by the unique interpenetrating interface design and precise control over active materials mass loading, owing to the optimized ink formulations and droplet-assisted printing processes. The sensors deliver sensitivities of 313.28 µA mm-1 cm-2 for glucose and 0.87 µA mm-1 cm-2 for alcohol sensing with minimal drift over 30 h, which are among the best in the literature. The integrated patch can be used for reliable and wireless diet monitoring or medical intervention via epidermal analysis and would inspire the advances of wearable devices for intelligent healthcare applications.

The combination of DNA nanostructures and materials for highly sensitive electrochemical detection

Due to the wide range of electrochemical devices available, DNA nanostructures and material-based technologies have been greatly broadened. They have been actively used to create a variety of beautiful nanostructures owing to their unmatched programmability. Currently, a variety of electrochemical devices have been used for rapid sensing of biomolecules and other diagnostic applications. Here, we provide a brief overview of recent advances in DNA-based biomolecular assays. Biosensing platform such as electrochemical biosensor, nanopore biosensor, and field-effect transistor biosensors (FET), which are equipped with aptamer, DNA walker, DNAzyme, DNA origami, and nanomaterials, has been developed for amplification detection. Under the optimal conditions, the proposed biosensor has good amplification detection performance. Further, we discussed the challenges of detection strategies in clinical applications and offered the prospect of this field.

A laser-Engraved Wearable Electrochemical Sensing Patch for Heat Stress Precise Individual Management of Horse

In point-of-care diagnostics, the continuous monitoring of sweat constituents provides a window into individual's physiological state. For species like horses, with abundant sweat glands, sweat composition can serve as an early health indicator. Considering the salience of such metrics in the domain of high-value animal breeding, a sophisticated wearable sensor patch tailored is introduced for the dynamic assessment of equine sweat, offering insights into pH, potassium ion (K+), and temperature profiles during episodes of heat stress and under normal physiological conditions. The device integrates a laser-engraved graphene (LEG) sensing electrode array, a non-invasive iontophoretic module for stimulated sweat secretion, an adaptable signal processing unit, and an embedded wireless communication framework. Profiting from an admirable Truth Table capable of logical evaluation, the integrated system enabled the early and timely assessment for heat stress, with high accuracy, stability, and reproducibility. The sensor patch has been calibrated to align with the unique dermal and physiological contours of equine anatomy, thereby augmenting its applicability in practical settings. This real-time analysis tool for equine perspiration stands to revolutionize personalized health management approaches for high-value animals, marking a significant stride in the integration of smart technologies within the agricultural sector.

Nutritional Profiles and Their Links to Insulin Resistance and Anthropometric Variables in a Female Cohort

This study investigates the relationship between dietary habits and metabolic health among women, emphasizing the role of anthropometric parameters as proxies for insulin resistance. We analyzed data from 443 women categorized into two groups based on the presence or absence of clinically diagnosed insulin resistance. Our assessments included dietary quality, socio-demographic characteristics, and a series of anthropometric measurements such as body weight, Body Mass Index (BMI), Waist-Hip Ratio (WHR), Abdominal Volume Index (AVI), and Body Adiposity Index (BAI). The results indicated significant disparities in these parameters, with the insulin-resistant group exhibiting higher average body weight (78.92 kg vs. 65.04 kg, p < 0.001), BMI (28.45 kg/m2 vs. 23.17 kg/m2, p < 0.001), and other related measures, suggesting a strong influence of dietary patterns on body composition and metabolic risk. The study underscores the importance of dietary management in addressing insulin resistance, advocating for personalized dietary strategies to improve metabolic health outcomes in women. This approach highlights the need for integrating dietary changes with lifestyle modifications and socio-demographic considerations to combat metabolic risks effectively.

Pursuing precision in medicine and nutrition: the rise of electrochemical biosensing at the molecular level

In the era that we seek personalization in material things, it is becoming increasingly clear that the individualized management of medicine and nutrition plays a key role in life expectancy and quality of life, allowing participation to some extent in our welfare and the use of societal resources in a rationale and equitable way. The implementation of precision medicine and nutrition are highly complex challenges which depend on the development of new technologies able to meet important requirements in terms of cost, simplicity, and versatility, and to determine both individually and simultaneously, almost in real time and with the required sensitivity and reliability, molecular markers of different omics levels in biofluids extracted, secreted (either naturally or stimulated), or circulating in the body. Relying on representative and pioneering examples, this review article critically discusses recent advances driving the position of electrochemical bioplatforms as one of the winning horses for the implementation of suitable tools for advanced diagnostics, therapy, and precision nutrition. In addition to a critical overview of the state of the art, including groundbreaking applications and challenges ahead, the article concludes with a personal vision of the imminent roadmap.

The Future of Food

The global food systems face significant challenges driven by population growth, climate change, geopolitical conflicts, crises, and evolving consumer preferences. Intending to address these challenges, optimizing food production, adopting sustainable practices, and developing technological advancements are essential while ensuring the safety and public acceptance of innovations. This review explores the complex aspects of the future of food, encompassing sustainable food production, food security, climate-resilient and digitalized food supply chain, alternative protein sources, food processing, and food technology, the impact of biotechnology, cultural diversity and culinary trends, consumer health and personalized nutrition, and food production within the circular bioeconomy. The article offers a holistic perspective on the evolving food industry characterized by innovation, adaptability, and a shared commitment to global food system resilience. Achieving sustainable, nutritious, and environmentally friendly food production in the future involves comprehensive changes in various aspects of the food supply chain, including innovative farming practices, evolving food processing technologies, and Industry 4.0 applications, as well as approaches that redefine how we consume food.

3D printing for space food applications: Advancements, challenges, and prospects

Space foods closely associate with the performance and mental health of astronauts. Over the years, a range of manufacturing technologies have been explored and advancements in food 3D printing can provide answers to certain existing challenges and revolutionize the way foods are prepared for space exploration missions. Apart from the nutrition and satiety perspective, product shelf-life, variety, personalization, and the need for customized diets are critical considerations. In such long-duration human-crewed space missions, under microgravity conditions and exposure to space, psychological factors heavily affect food consumption patterns. Therefore, there has been a surge in research funding for developing products and methods that offer safe, nutritionally balanced, and delightful food options. 3D food printing could be a creative solution for such requirements. While multiple challenges must be addressed, the technology promises waste minimization and the scope for on-site on-demand food preparation. This article begins with fundamental concepts of this subject, provides a timeline of the advancements in the field, and details the futuristic prospects of the technology for long-duration space missions.

Current insights into genome-based personalized nutrition technology: a patent review

Unlike general nutritional ranges that meet the nutritional needs essential for maintaining the life of an entire population, personalized nutrition is characterised by maintaining health through providing customized nutrition according to individuals' lifestyles or genetic characteristics. The development of technology and services for personalized nutrition is increasing, owing to the acquisition of knowledge about the differences in nutritional requirements according to the diversity of individuals and an increase in health interest. Regarding genetics, technology is being developed to distinguish the various characteristics of individuals and provide customized nutrition. Therefore, to understand the current state of personalized nutrition technology, understanding genomics is necessary to acquire information on nutrition research based on genomics. We reviewed patents related to personalized nutrition-targeting genomics and examined their mechanisms of action. Using the patent database, we searched 694 patents on nutritional genomics and extracted 561 highly relevant valid data points. Furthermore, an in-depth review was conducted by selecting core patents related to genome-based personalized nutrition technology. A marked increase was observed in personalized nutrition technologies using methods such as genetic scoring and disease-specific dietary recommendations.

Rational Design of Flexible Mechanical Force Sensors for Healthcare and Diagnosis

Over the past decade, there has been a significant surge in interest in flexible mechanical force sensing devices and systems. Tremendous efforts have been devoted to the development of flexible mechanical force sensors for daily healthcare and medical diagnosis, driven by the increasing demand for wearable/portable devices in long-term healthcare and precision medicine. In this review, we summarize recent advances in diverse categories of flexible mechanical force sensors, covering piezoresistive, capacitive, piezoelectric, triboelectric, magnetoelastic, and other force sensors. This review focuses on their working principles, design strategies and applications in healthcare and diagnosis, with an emphasis on the interplay among the sensor architecture, performance, and application scenario. Finally, we provide perspectives on the remaining challenges and opportunities in this field, with particular discussions on problem-driven force sensor designs, as well as developments of novel sensor architectures and intelligent mechanical force sensing systems.

Innovative strategies to predict and prevent the risk for malnutrition in child, adolescent, and young adult cancer survivors

Children, adolescents, and young adult cancer survivors (CAYAs) constitute a growing population requiring a customized approach to mitigate the incidence of severe complications throughout their lifetimes. During cancer treatment, CAYAs cancer survivors undergo significant disruptions in their nutritional status, elevating the risks of mortality, morbidity, and cardiovascular events. The assessment of nutritional status during cancer treatment involves anthropometric and dietary evaluations, emphasizing the necessity for regular assessments and the timely identification of risk factors. Proactive nutritional interventions, addressing both undernutrition and overnutrition, should be tailored to specific age groups and incorporate a family-centered approach. Despite encouraging interventions, a notable evidence gap persists. The goal of this review is to comprehensively examine the existing evidence on potential nutritional interventions for CAYAs cancer survivors. We explore the evidence so far collected on the nutritional intervention strategies elaborated for CAYAs cancer survivors that should target both undernutrition and overnutrition, being age-specific and involving a family-based approach. Furthermore, we suggest harnessing artificial intelligence (AI) to anticipate and prevent malnutrition in CAYAs cancer survivors, contributing to the identification of novel risk factors and promoting proactive, personalized healthcare.

Access and Management of Sweat for Non-Invasive Biomarker Monitoring: A Comprehensive Review

Sweat is an important biofluid presents in the body since it regulates the internal body temperature, and it is relatively easy to access on the skin unlike other biofluids and contains several biomarkers that are also present in the blood. Although sweat sensing devices have recently displayed tremendous progress, most of the emerging devices primarily focus on the sensor development, integration with electronics, wearability, and data from in vitro studies and short-term on-body trials during exercise. To further the advances in sweat sensing technology, this review aims to present a comprehensive report on the approaches to access and manage sweat from the skin toward improved sweat collection and sensing. It is begun by delineating the sweat secretion mechanism through the skin, and the historical perspective of sweat, followed by a detailed discussion on the mechanisms governing sweat generation and management on the skin. It is concluded by presenting the advanced applications of sweat sensing, supported by a discussion of robust, extended-operation epidermal wearable devices aiming to strengthen personalized healthcare monitoring systems.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

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.

Nutri-microbiome epidemiology, an emerging field to disentangle the interplay between nutrition and microbiome for human health

Diet and nutrition have a substantial impact on the human microbiome, and interact with the microbiome, especially gut microbiome, to modulate various diseases and health status. Microbiome research has also guided the nutrition field to a more integrative direction, becoming an essential component of the rising area of precision nutrition. In this review, we provide a broad insight into the interplay among diet, nutrition, microbiome, and microbial metabolites for their roles in the human health. Among the microbiome epidemiological studies regarding the associations of diet and nutrition with microbiome and its derived metabolites, we summarize those most reliable findings and highlight evidence for the relationships between diet and disease-associated microbiome and its functional readout. Then, the latest advances of the microbiome-based precision nutrition research and multidisciplinary integration are described. Finally, we discuss several outstanding challenges and opportunities in the field of nutri-microbiome epidemiology.

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions

PURPOSE OF REVIEW

The aim of this review is to provide an overview of the use of commercial wrist-worn mobile health devices to track and monitor physiological outcomes in behavioral interventions as well as discuss considerations for selecting the optimal device.

RECENT FINDINGS

Wearable technology can enhance intervention design and implementation. The use of wrist-worn wearables provides the opportunity for tracking physiological outcomes, thus providing a unique approach for assessment and delivery of remote interventions. Recent findings support the utility, acceptability, and benefits of commercial wrist-worn wearables in interventions, and they can be used to continuously monitor outcomes, remotely administer assessments, track adherence, and personalize interventions. Wrist-worn devices show acceptable accuracy when measuring heart rate, blood pressure, step counts, and physical activity; however, accuracy is dependent on activity type, intensity, and device brand. These factors should be considered when designing behavioral interventions that utilize wearable technology.

SUMMARY

With the continuous advancement in technology and frequent product upgrades, the capabilities of commercial wrist-worn devices will continue to expand, thus increasing their potential use in intervention research. Continued research is needed to examine and validate the most recent devices on the market to better inform intervention design and implementation.

Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors

BACKGROUND

Gestational diabetes mellitus (GDM) is an increasing health risk for pregnant women as well as their children. Telehealth interventions targeted at the management of GDM have been shown to be effective, but they still require health care professionals for providing guidance and feedback. Feedback from wearable sensors has been suggested to support the self-management of GDM, but it is unknown how self-tracking should be designed in clinical care.

OBJECTIVE

This study aimed to investigate how to support the self-management of GDM with self-tracking of continuous blood glucose and lifestyle factors without help from health care personnel. We examined comprehensive self-tracking from self-discovery (ie, learning associations between glucose levels and lifestyle) and user experience perspectives.

METHODS

We conducted a mixed methods study where women with GDM (N=10) used a continuous glucose monitor (CGM; Medtronic Guardian) and 3 physical activity sensors: activity bracelet (Garmin Vivosmart 3), hip-worn sensor (UKK Exsed), and electrocardiography sensor (Firstbeat 2) for a week. We collected data from the sensors, and after use, participants took part in semistructured interviews about the wearable sensors. Acceptability of the wearable sensors was evaluated with the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Moreover, maternal nutrition data were collected with a 3-day food diary, and self-reported physical activity data were collected with a logbook.

RESULTS

We found that the CGM was the most useful sensor for the self-discovery process, especially when learning associations between glucose and nutrition intake. We identified new challenges for using data from the CGM and physical activity sensors in supporting self-discovery in GDM. These challenges included (1) dispersion of glucose and physical activity data in separate applications, (2) absence of important trackable features like amount of light physical activity and physical activities other than walking, (3) discrepancy in the data between different wearable physical activity sensors and between CGMs and capillary glucose meters, and (4) discrepancy in perceived and measured quantification of physical activity. We found the body placement of sensors to be a key factor in measurement quality and preference, and ultimately a challenge for collecting data. For example, a wrist-worn sensor was used for longer compared with a hip-worn sensor. In general, there was a high acceptance for wearable sensors.

CONCLUSIONS

A mobile app that combines glucose, nutrition, and physical activity data in a single view is needed to support self-discovery. The design should support tracking features that are important for women with GDM (such as light physical activity), and data for each feature should originate from a single sensor to avoid discrepancy and redundancy. Future work with a larger sample should involve evaluation of the effects of such a mobile app on clinical outcomes.

TRIAL REGISTRATION

Clinicaltrials.gov NCT03941652; https://clinicaltrials.gov/study/NCT03941652.

NEmecum: digital tool for assisting in the prescription and dispensing of enteral nutrition formulas and infant preparations

Introduction

Introduction: there is a wide variety of enteral nutrition and infant formulas preparations. When there is a need to find infomation on a product, only the infomation from industy is available. Comparison amomg products becomes then ardous. Objective: to describe the development of NEmecum as the first website that allows a directed and independent search for enteral nutrition products and infant formulas, currently available in Spain, and to evaluate the initial use of NEmecum. Methods: the structure of a database that unifies the parameters of all formulas was developed, and the nutritional composition of all formulas was analyzed. Subsequently, the main search algorithms were selected and the digital tool was codified. Intensive dissemination was performed and the impact was evaluated. The profile of users and registered centers and the use of the tool were analyzed, and its usability was evaluated using the System Usability Scale (SUS) questionnaire. Results: a free-access responsive website (http://nemecum.com) that allows searches based on pre-established search filters was obtained. This tool allows for a detailed analysis avalaible formulas in Spain by observing a wide variety of formulas with similar characteristics. The dissemination campaign managed to increase its use exponentially, currently reaching 1,370 users and 79 registered centers. Usability was rated as excellent. Conclusion: the development of the NEmecum represents a valuable tool in the search and consultation of the characteristics of enteral nutrition formulas and infant preparations.

Taste Bud-Inspired Single-Drop Multitaste Sensing for Comprehensive Flavor Analysis with Deep Learning Algorithms

The electronic tongue (E-tongue) system has emerged as a significant innovation, aiming to replicate the complexity of human taste perception. In spite of the advancements in E-tongue technologies, two primary challenges remain to be addressed. First, evaluating the actual taste is complex due to interactions between taste and substances, such as synergistic and suppressive effects. Second, ensuring reliable outcomes in dynamic conditions, particularly when faced with high deviation error data, presents a significant challenge. The present study introduces a bioinspired artificial E-tongue system that mimics the gustatory system by integrating multiple arrays of taste sensors to emulate taste buds in the human tongue and incorporating a customized deep-learning algorithm for taste interpretation. The developed E-tongue system is capable of detecting four distinct tastes in a single drop of dietary compounds, such as saltiness, sourness, astringency, and sweetness, demonstrating notable reversibility and selectivity. The taste profiles of six different wines are obtained by the E-tongue system and demonstrated similarities in taste trends between the E-tongue system and user reviews from online, although some disparities still exist. To mitigate these disparities, a prototype-based classifier with soft voting is devised and implemented for the artificial E-tongue system. The artificial E-tongue system achieved a high classification accuracy of ∼95% in distinguishing among six different wines and ∼90% accuracy even in an environment where more than 1/3 of the data contained errors. Moreover, by harnessing the capabilities of deep learning technology, a recommendation system was demonstrated to enhance the user experience.

Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review

To monitor adherence to diets and to design and evaluate nutritional interventions, it is essential to obtain objective knowledge about eating behavior. In most research, measures of eating behavior are based on self-reporting, such as 24-h recalls, food records (food diaries) and food frequency questionnaires. Self-reporting is prone to inaccuracies due to inaccurate and subjective recall and other biases. Recording behavior using nonobtrusive technology in daily life would overcome this. Here, we provide an up-to-date systematic overview encompassing all (close-to) publicly or commercially available technologies to automatically record eating behavior in real-life settings. A total of 1328 studies were screened and, after applying defined inclusion and exclusion criteria, 122 studies were included for in-depth evaluation. Technologies in these studies were categorized by what type of eating behavior they measure and which type of sensor technology they use. In general, we found that relatively simple sensors are often used. Depending on the purpose, these are mainly motion sensors, microphones, weight sensors and photo cameras. While several of these technologies are commercially available, there is still a lack of publicly available algorithms that are needed to process and interpret the resulting data. We argue that future work should focus on developing robust algorithms and validating these technologies in real-life settings. Combining technologies (e.g., prompting individuals for self-reports at sensed, opportune moments) is a promising route toward ecologically valid studies of eating behavior.

Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems (APNASs)

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

Revealing the Acute Effects of Dietary Components on Mood and Cognition: The Role of Autonomic Nervous System Responses

A growing body of literature suggests dietary components can support mood and cognitive function through the impact of their bioactive or sensorial properties on neural pathways. Of interest, objective measures of the autonomic nervous system-such as those regulating bodily functions related to heartbeat and sweating-can be used to assess the acute effects of dietary components on mood and cognitive function. Technological advancements in the development of portable and wearable devices have made it possible to collect autonomic responses in real-world settings, creating an opportunity to study how the intake of dietary components impacts mood and cognitive function at an individual level, day-to-day. In this paper, we aimed to review the use of autonomic nervous system responses such as heart rate or skin galvanic response to investigate the acute effects of dietary components on mood and cognitive performance in healthy adult populations. In addition to examining the existing methodologies, we also propose new state-of-the-art techniques that use autonomic nervous system responses to detect changes in proxy patterns for the automatic detection of stress, alertness, and cognitive performance. These methodologies have potential applications for home-based nutrition interventions and personalized nutrition, enabling individuals to recognize the specific dietary components that impact their mental and cognitive health and tailor their nutrition accordingly.

Metal-organic frameworks (MOFs) based luminescent and electrochemical sensors for food contaminant detection

With the increasing population, food toxicity has become a prevalent concern due to the growing contaminants of food products. Therefore, the need for new materials for toxicant detection and food quality monitoring will always be in demand. Metal-organic frameworks (MOFs) based on luminescence and electrochemical sensors with tunable porosity and active surface area are promising materials for food contaminants monitoring. This review summarizes and studies the most recent progress on MOF sensors for detecting food contaminants such as pesticides, antibiotics, toxins, biomolecules, and ionic species. First, with the introduction of MOFs, food contaminants and materials for toxicants detection are discussed. Then the insights into the MOFs as emerging materials for sensing applications with luminescent and electrochemical properties, signal changes, and sensing mechanisms are discussed. Next, recent advances in luminescent and electrochemical MOFs food sensors and their sensitivity, selectivity, and capacities for common food toxicants are summarized. Further, the challenges and outlooks are discussed for providing a new pathway for MOF food contaminant detection tools. Overall, a timely source of information on advanced MOF materials provides materials for next-generation food sensors.

Fluid-based wearable sensors: a turning point in personalized healthcare

Nowadays, it has become very popular to develop wearable devices that can monitor biomarkers to analyze the health status of the human body more comprehensively and accurately. Wearable sensors, specially designed for home care services, show great promise with their ease of use, especially during pandemic periods. Scientists have conducted many innovative studies on new wearable sensors that can noninvasively and simultaneously monitor biochemical indicators in body fluids for disease prediction, diagnosis, and management. Using noninvasive electrochemical sensors, biomarkers can be detected in tears, saliva, perspiration, and skin interstitial fluid (ISF). In this review, biofluids used for noninvasive wearable sensor detection under four main headings, saliva, sweat, tears, and ISF-based wearable sensors, were examined in detail. This report analyzes nearly 50 recent articles from 2017 to 2023. Based on current research, this review also discusses the evolution of wearable sensors, potential implementation challenges, and future prospects.

Insulin detection in diabetes mellitus: challenges and new prospects

Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.

Biomolecular sensors for advanced physiological monitoring

Body-based biomolecular sensing systems, including wearable, implantable and consumable sensors allow comprehensive health-related monitoring. Glucose sensors have long dominated wearable bioanalysis applications owing to their robust continuous detection of glucose, which has not yet been achieved for other biomarkers. However, access to diverse biological fluids and the development of reagentless sensing approaches may enable the design of body-based sensing systems for various analytes. Importantly, enhancing the selectivity and sensitivity of biomolecular sensors is essential for biomarker detection in complex physiological conditions. In this Review, we discuss approaches for the signal amplification of biomolecular sensors, including techniques to overcome Debye and mass transport limitations, and selectivity improvement, such as the integration of artificial affinity recognition elements. We highlight reagentless sensing approaches that can enable sequential real-time measurements, for example, the implementation of thin-film transistors in wearable devices. In addition to sensor construction, careful consideration of physical, psychological and security concerns related to body-based sensor integration is required to ensure that the transition from the laboratory to the human body is as seamless as possible.

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

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.

A Tailored and Engaging mHealth Gamified Framework for Nutritional Behaviour Change

Mobile health applications (apps) have been shown to be effective for improving eating habits. However, most of the existing apps rely on calorie and nutrient counting which have several limitations including the difficulty in sustaining long-term use, inaccuracy, and the risk of developing eating disorders. We designed and developed a mHealth framework for nutritional behaviour change, integrated into the CarpeDiem app, that focuses on the intake of key food groups which are known to have a higher impact on health indicators instead of the intake of nutrients. This framework is mainly based on a gamified system that delivers personalized dietary missions to the user and provides motivational recommendations that help the user to achieve these missions. Its design was guided by an evidenced-based theory of behavioural change, the HAPA model, and it is also characterized by the personalization of the system and the use of a recommender system based on advanced artificial intelligence techniques. Overall, the approach used in the present app could foster a sustained improvement of eating habits among the general population, which is the main challenge of dietary interventions, decreasing the risk of developing the chronic diseases associated with unhealthy dietary habits.

Rh single-atom nanozymes for efficient ascorbic acid oxidation and detection

The management of ascorbic acid (AA) in biological fluids is of significant importance for body functions and human health, yet challenging due to the lack of high-performance sensing catalysts. Herein, we report the design of Rh single-atom nanozymes (Rh SAzymes) by mimicking the active sites of ascorbate peroxidase toward efficient electrocatalytic oxidation and detection of AA. Benefiting from the enzyme-mimicking single-atom coordination, the Rh SAzyme exhibits an unprecedented electrocatalytic activity for AA oxidation with an onset potential as low as 0.02 V (vs. Ag/AgCl). Combined with the screen-printing technology, a miniaturized Rh SAzyme biosensor was firstly constructed for tracking dynamic trends of AA in the human subject and detecting AA content in nutritional products. The as-prepared biosensor exhibits excellent detection performances with a wide linear range of 10.0 μM-53.1 mM, a low detection limit of 0.26 μM, and a long stability of 28 days. This work opens a door for the design of artificial single-atom electrocatalysts to mimic natural enzymes and their subsequent application in biosensors.

Conformational-switch biosensors as novel tools to support continuous, real-time molecular monitoring in lab-on-a-chip devices

Recent years have seen continued expansion of the functionality of lab on a chip (LOC) devices. Indeed LOCs now provide scientists and developers with useful and versatile platforms across a myriad of chemical and biological applications. The field still fails, however, to integrate an often important element of bench-top analytics: real-time molecular measurements that can be used to "guide" a chemical response. Here we describe the analytical techniques that could provide LOCs with such real-time molecular monitoring capabilities. It appears to us that, among the approaches that are general (i.e., that are independent of the reactive or optical properties of their targets), sensing strategies relying on binding-induced conformational change of bioreceptors are most likely to succeed in such applications.

A new paradigm of reliable sensing with field-deployed electrochemical sensors integrating data redundancy and source credibility

For a continuous healthcare or environmental monitoring system, it is essential to reliably sense the analyte concentration reported by electrochemical sensors. However, environmental perturbation, sensor drift, and power-constraint make reliable sensing with wearable and implantable sensors difficult. While most studies focus on improving sensor stability and precision by increasing the system's complexity and cost, we aim to address this challenge using low-cost sensors. To obtain the desired accuracy from low-cost sensors, we borrow two fundamental concepts from communication theory and computer science. First, inspired by reliable data transmission over a noisy communication channel by incorporating redundancy, we propose to measure the same quantity (i.e., analyte concentration) with multiple sensors. Second, we estimate the true signal by aggregating the output of the sensors based on their credibility, a technique originally developed for "truth discovery" in social sensing applications. We use the Maximum Likelihood Estimation to estimate the true signal and the credibility index of the sensors over time. Using the estimated signal, we develop an on-the-fly drift-correction method to make unreliable sensors reliable by correcting any systematic drifts during operation. Our approach can determine solution pH within 0.09 pH for more than three months by detecting and correcting the gradual drift of pH sensors as a function of gamma-ray irradiation. In the field study, we validate our method by measuring nitrate levels in an agricultural field onsite over 22 days within 0.06 mM of a high-precision laboratory-based sensor. We theoretically demonstrate and numerically validate that our approach can estimate the true signal even when the majority (~ 80%) of the sensors are unreliable. Moreover, by restricting wireless transmission to high-credible sensors, we achieve near-perfect information transfer at a fraction of the energy cost. The high-precision sensing with low-cost sensors at reduced transmission cost will pave the way for pervasive in-field sensing with electrochemical sensors. The approach is general and can improve the accuracy of any field-deployed sensors undergoing drift and degradation during operation.

A Comprehensive Study: Traditional and Cutting-Edge Analytical Techniques for the Biomarker Based Detection of the Micronutrients & POC Sensing Directions for Next-Generation Diagnostic

Micronutrient deficiency is wide spread and highly affects morbidity, mortality, and well-being of human beings. Micronutrient deficiency gradually manifests into diseases, which effects pathophysiology directly or indirectly. There is an imprecision in the diagnosis of micronutrient deficiency because of two causes; the selection of the standard biomarker and the diagnostic technique used. In appropriate diagnosis could increase the severity of the disorder. Instead of a single a combination of biomarkers can give more stringent results for micronutrient testing. Several traditional analytical techniques are used for diagnosis but HPLC, ELISA & LCMS/MS are most sensitive and reliable methods used by CLSIA-certified labs. However, these techniques require well-equipped, centralized laboratory facilities. The diagnostic era moves toward the Point of Care Testing (POCT), a boon in emerging diagnostics, breaking all paradigms of traditional analytical techniques. POCT led us toward the development of biosensors, which encompasses many techniques like paper-based sensors, microfluidic chip, wearable devices, and smartphone-assisted diagnostics, which become more popular diagnostic tools. This outlook summarizes the micronutrients like vitamins A, B5, B6, B7, B9, B12 C, D, and E and Minerals like iron, calcium, zinc, magnesium, and sodium; along with its biomarkers, analytical techniques, and point of care innovation in micronutrients.

Natural oxidase-mimicking copper-organic frameworks for targeted identification of ascorbate in sensitive sweat sensing

Sweat sensors play a significant role in personalized healthcare by dynamically monitoring biochemical markers to detect individual physiological status. The specific response to the target biomolecules usually depends on natural oxidase, but it is susceptible to external interference. In this work, we report tryptophan- and histidine-treated copper metal-organic frameworks (Cu-MOFs). This amino-functionalized copper-organic framework shows highly selective activity for ascorbate oxidation and can serve as an efficient ascorbate oxidase-mimicking material in sensitive sweat sensors. Experiments and calculation results elucidate that the introduced tryptophan/histidine fundamentally regulates the adsorption behaviors of biomolecules, enabling ascorbate to be selectively captured from complex sweat and further efficiently electrooxidized. This work provides not only a paradigm for specifically sweat sensing but also a significant understanding of natural oxidase-inspired MOF nanoenzymes for sensing technologies and beyond.

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

Self-Testing of Ketone Bodies, along with Glucose, Using Touch-Based Sweat Analysis

β-Hydroxybutyrate (HB) is one of the main physiological ketone bodies that play key roles in human health and wellness. Besides their important role in diabetes ketoacidosis, ketone bodies are currently receiving tremendous attention for personal nutrition in connection to the growing popularity of oral ketone supplements. Accordingly, there are urgent needs for developing a rapid, simple, and low-cost device for frequent onsite measurements of β-hydroxybutyrate (HB), one of the main physiological ketone bodies. However, real-time profiling of dynamically changing HB concentrations is challenging and still limited to laboratory settings or to painful and invasive measurements (e.g., a commercial blood ketone meter). Herein, we address the critical need for pain-free frequent HB measurements in decentralized settings and report on a reliable noninvasive, simple, and rapid touch-based sweat HB testing and on its ability to track dynamic HB changes in secreted fingertip sweat, following the intake of commercial ketone supplements. The new touch-based HB detection method relies on an instantaneous collection of the fingertip sweat at rest on a porous poly(vinyl alcohol) (PVA) hydrogel that transports the sweat to a biocatalytic layer, composed of the β-hydroxybutyrate dehydrogenase (HBD) enzyme and its nicotinamide adenine dinucleotide (NAD⁺) cofactor, covering the modified screen-printed carbon working electrode. As a result, the sweat HB can be measured rapidly by the mediated oxidation reaction of the nicotinamide adenine dinucleotide (NADH) product. A personalized HB dose-response relationship is demonstrated within a group of healthy human subjects taking commercial ketone supplements, along with a correlation between the sweat and capillary blood HB levels. Furthermore, a dual disposable biosensing device, consisting of neighboring ketone and glucose enzyme electrodes on a single-strip substrate, has been developed toward the simultaneous touch-based detection of dynamically changing sweat HB and glucose levels, following the intake of ketone and glucose drinks.

Electrochemical biosensors based on saliva electrolytes for rapid detection and diagnosis

In recent years, electrochemical biosensors (ECBSs) have shown significant potential for real-time disease diagnosis and in situ physical condition monitoring. As a multi-constituent oral fluid comprising various disease signaling biomarkers, saliva has drawn much attention in the field of point-of-care (POC) testing. In particular, during the outbreak of the COVID-19 pandemic, ECBSs which hold the simplicity of a single-step assay compared with the multi-step assay of traditional testing methods are expected to relieve the human and economic burden caused by the massive and long-term sample testing process. Noteworthily, ECBSs for the detection of SARS-CoV-2 in saliva have already been developed and may replace current testing methods. Furthermore, the detection scope has expanded from routine indices such as sugar and uric acid to abnormal biomarkers for early-stage disease detection and drug level monitoring, which further facilitated the evolution of ECBSs in the last 5 years. This review is divided into several main sections. First, we discussed the latest advancements and representative research on ECBSs for saliva testing. Then, we focused on a novel kind of ECBS, organic electrochemical transistors (OECTs), which hold great advantages of high sensitivity and signal-to-noise ratio and on-site detection. Finally, application of ECBSs with integrated portable platforms in oral cavities, which lead to powerful auxiliary testing means for telemedicine, has also been discussed.

Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies

BACKGROUND

Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research.

OBJECTIVE

This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications.

METHODS

We conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized and categorized the AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques.

RESULTS

We identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes. The studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority (18/22, 82%) of the studies comparing AI models with conventional statistical approaches found that the AI models achieved higher prediction accuracy on test data. Some (5/46, 11%) of the studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the data set and task it was applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML models was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review.

CONCLUSIONS

This study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends such as multimodal or multitask AI models, synthetic data generation, and human-in-the-loop that may witness increasing applications in obesity research.

A wearable electrochemical biosensor for the monitoring of metabolites and nutrients

Wearable non-invasive biosensors for the continuous monitoring of metabolites in sweat can detect a few analytes at sufficiently high concentrations, typically during vigorous exercise so as to generate sufficient quantity of the biofluid. Here we report the design and performance of a wearable electrochemical biosensor for the continuous analysis, in sweat during physical exercise and at rest, of trace levels of multiple metabolites and nutrients, including all essential amino acids and vitamins. The biosensor consists of graphene electrodes that can be repeatedly regenerated in situ, functionalized with metabolite-specific antibody-like molecularly imprinted polymers and redox-active reporter nanoparticles, and integrated with modules for iontophoresis-based sweat induction, microfluidic sweat sampling, signal processing and calibration, and wireless communication. In volunteers, the biosensor enabled the real-time monitoring of the intake of amino acids and their levels during physical exercise, as well as the assessment of the risk of metabolic syndrome (by correlating amino acid levels in serum and sweat). The monitoring of metabolites for the early identification of abnormal health conditions could facilitate applications in precision nutrition.

Integrating magnetic metal-organic frameworks-based sample preparation with microchannel resistance biosensor for rapid and quantitative detection of aflatoxin B1

Aflatoxin B₁, a secondary metabolite produced by fungi, is one of the most toxic mycotoxins that poses a major food security and public health threat worldwide. Effective sample pretreatment and high sensitivity detection techniques are urgently needed due to its trace amount in complex samples. Herein, an integrated detection strategy was developed by combining Mg/Zn-metal organic framework-74 modified Fe₃O₄ magnetic nanoparticles (Mg/Zn-MOF-74 @Fe₃O₄ MNPs)-based sample preparation and microchannel resistance biosensor for rapid and highly sensitive detection of aflatoxin B₁ in food samples. The synthesis and characterization of Mg/Zn-MOF-74 @Fe₃O₄ MNPs was reported, which exhibited efficient separation and enrichment capacity when exposed to complex grain samples. The competitive immunoassay-based microchannel resistance biosensor enabled specific and high-sensitive analysis of aflatoxin B₁ by using current as a readout, which caused by the blocking effect between the functionalized-polystyrene microspheres and microchannel. Under optimized conditions, this biosensor was capable to quantitatively analysis aflatoxin B₁ from 10 pg/mL to 20 ng/mL, and with a limit of detection of 4.75 pg/mL. This integrated detection strategy has been tested for the quantitative detection of aflatoxin B₁ in grain samples that is a potential protocol for food safety control and environmental monitoring.

Utilizing Gradient Porous Graphene Substrate as the Solid-Contact Layer To Enhance Wearable Electrochemical Sweat Sensor Sensitivity

Wearable sweat monitoring represents an attractive opportunity for personalized healthcare and for evaluating sports performance. One of the limitations with such monitoring, however, is water layer formation upon cycling of ion-selective sensors, leading to degraded sensitivity and long-term instability. Our report is the first to use chemical vapor deposition-grown, three-dimensional, graphene-based, gradient porous electrodes to minimize such water layer formation. The proposed design reduces the ion diffusion path within the polymeric ion-selective membrane and enhances the electroactive surface for highly sensitive, real-time detection of Na⁺ ions in human sweat with high selectivity. We obtained a 7-fold enhancement in electroactive surface against 2D electrodes (e.g., carbon, gold), yielding a sensitivity of 65.1 ± 0.25 mV decade^-1 (n = 3, RSD = 0.39%), the highest to date for wearable Na⁺ sweat sensors. The on-body sweat sensing performance is comparable to that of ICP-MS, suggesting its feasibility for health evaluation through sweat.

A high-conductive, anti-freezing, antibacterial and anti-swelling starch-based physical hydrogel for multifunctional flexible wearable sensors

Flexible wearable sensors based on conductive hydrogels are attracting increasing interest. To meet the urgent demands of sustainability and eco-friendliness, biopolymer-based physically crosslinked hydrogels have drawn great attention. Starch has a great potential due to its renewability, biocompatibility, nontoxicity and low cost. However, poor mechanical property, low conductivity and lack of versatility are seriously limiting the applications of starch-based hydrogels in wearable sensors. Moreover, the development of starch hydrogel-based wearable sensors in harsh conditions remains a challenge. Herein, multifunctional and physical crosslinking hydrogels were developed by introducing ionic liquid (1-ethyl-3-methyl imidazolium acetate) and metal salt (AlCl₃) into starch/polyvinyl alcohol double-network structure. The hydrogel exhibited excellent stretchability (567%), tensile strength (0.53 MPa), high conductivity (2.75 S·m^-1), good anti-freezing, antibacterial and anti-swelling properties. A wearable sensor assembled from the starch-based hydrogel exhibited a wide working range, high sensitivity (gauge factor: 5.93) and excellent reversibility. Due to the versatility, the sensor effectively detected human motion in normal and underwater environment, and possessed a sensitive pressure and thermal response. Overall, the present work provided a promising route to develop multifunctional and "green" biopolymer-based hydrogels for wearable sensors in human health and sporting applications.

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.