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In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier's error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL's effectiveness and efficiency in identifying diseases is evaluated and compared., (© 2024. The Author(s).)

Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers' attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0., Competing Interests: Declaration of Competing Interest None, (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

3D printing, also known as Additive manufacturing (AM), has emerged as a transformative technology with applications across various industries, including the medical sector. This review paper provides an overview of the current status of AM technology, its challenges, and its application in the medical industry. The paper covers the different types of AM technologies, such as fused deposition modeling, stereolithography, selective laser sintering, digital light processing, binder jetting, and electron beam melting, and their suitability for medical applications. The most commonly used biomedical materials in AM, such as plastic, metal, ceramic, composite, and bio-inks, are also viewed. The challenges of AM technology, such as material selection, accuracy, precision, regulatory compliance, cost and quality control, and standardization, are also discussed. The review also highlights the various applications of AM in the medical sector, including the production of patient-specific surgical guides, prosthetics, orthotics, and implants. Finally, the review highlights the Internet of Medical Things (IoMT) and artificial intelligence (AI) for regulatory frameworks and safety standards for 3D-printed biomedical devices. The review concludes that AM technology can transform the healthcare industry by enabling patients to access more personalized and reasonably priced treatment alternatives. Despite the challenges, integrating AI and IoMT with 3D printing technology is expected to play a vital role in the future of biomedical device applications, leading to further advancements and improvements in patient care. More research is needed to address the challenges and optimize its use for medical applications to utilize AM's potential in the medical industry fully., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare settings, food control and monitoring, and environmental monitoring. In the past, conventional biosensors that have employed bioreceptors, such as enzymes, antibodies, Nucleic Acid (NA), etc., and used different transduction methods such as optical, thermal, electrochemical, electrical and magnetic detection, have been developed. Yet, with all the progresses made so far, these biosensors are clouded with many challenges, such as interference with undesirable compound, low sensitivity, specificity, selectivity, and longer processing time. In order to address these challenges, there is high need for developing novel, fast, highly sensitive biosensors with high accuracy and specificity. Scientists explore these gaps by incorporating nanoparticles (NPs) and nanocomposites (NCs) to enhance the desired properties. Graphene nanostructures have emerged as one of the ideal materials for biosensing technology due to their excellent dispersity, ease of functionalization, physiochemical properties, optical properties, good electrical conductivity, etc. The Integration of the Internet of Medical Things (IoMT) in the development of biosensors has the potential to improve diagnosis and treatment of diseases through early diagnosis and on time monitoring. The outcome of this comprehensive review will be useful to understand the significant role of graphene-based electrochemical biosensor integrated with Artificial Intelligence AI and IoMT for clinical diagnostics. The review is further extended to cover open research issues and future aspects of biosensing technology for diagnosis and management of clinical diseases and performance evaluation based on Linear Range (LR) and Limit of Detection (LOD) within the ranges of Micromolar µM (10 -6 ), Nanomolar nM (10 -9 ), Picomolar pM (10 -12 ), femtomolar fM (10 -15 ), and attomolar aM (10 -18 ).

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the "Internet of Medical Things" (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.

In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) holds immense promise for revolutionizing data analytics and decision-making processes. Current techniques for personalized medicine, disease diagnosis, treatment recommendations, and resource optimization in the Internet of Medical Things (IoMT) vary widely, including methods such as rule-based systems, machine learning algorithms, and data-driven approaches. However, many of these techniques face limitations in accuracy, scalability, and adaptability to complex clinical scenarios. This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT. Through the integration of advanced data analytics methodologies with NLP capabilities, we propose a comprehensive framework designed to enhance personalized medicine, streamline disease diagnosis, provide treatment recommendations, and optimize resource allocation. Using a systematic methodology data was collected from open data repositories, then preprocessed using data cleaning, missing value imputation, feature engineering, and data normalization and scaling. Optimization algorithms, such as Gradient Descent, Adam Optimization, and Stochastic Gradient Descent, were employed in the framework to enhance model performance. These were integrated with NLP processes, including Text Preprocessing, Tokenization, and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices. Lastly, through a synthesis of existing research and real-world case studies, we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency. The simulation produced compelling results, achieving an average diagnostic accuracy of 93.5% for the given scenarios, and excelled even further in instances involving rare diseases, achieving an accuracy rate of 98%. With regard to patient-specific treatment plans it generated them with an average precision of 96.7%. Improvements in early risk stratification and enhanced documentation were also noted. Furthermore, the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes, offering insights into risk-mitigating strategies. This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics, with implications for healthcare practitioners, researchers, and policymakers. By leveraging AI and NLP technologies in IoMT environments, this study paves the way for innovative strategies to enhance patient care and operational effectiveness. Ultimately, this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare. [ABSTRACT FROM AUTHOR]

The fusion of Internet of Things (IoT) technology into healthcare, known as the Internet of Medical Things (IoMT), has significantly enhanced medical treatment and operational efficiency. Real-time patient monitoring (RPM) and remote diagnostics enabled by IoMT allow doctors to treat more patients effectively and save lives. However, healthcare devices' interconnected nature makes them vulnerable to cyber-attacks, threatening patient privacy and security. Ensuring the security and accuracy of patient health data is paramount, as any tampering could have life-threatening consequences, especially in emergency situations. To address these challenges, this research focuses on developing robust security models to secure patient data in IoMT networks while meeting the growing demand for efficient healthcare services. Artificial intelligence (AI)-based technologies such as machine learning (ML) and deep learning (DL) have the potential to be employed as the methodology for intrusion detection. The goal of this research is threefold: firstly, the linear support vector machine (LinSVM) model; secondly, the convolutional support vector machine (ConvSVM) model; and finally, the categorical embedding (CatEmb) model, which have been proposed to overcome the issue of security in a network. This article offers the CatEmb model as the first effort to use a DL-based embedding approach to recognize intrusion in the IoMT environment, utilizing patient biometric and network traffic flow data. Our experimental results show the efficacy of the proposed DL models, with the LinSVM achieving a training accuracy of 99.78%, ConvSVM reaching 99.98%, and CatEmb achieving 99.84%. These models outperform existing methodologies by 2.61% in detecting network intrusions, as demonstrated through metrics such as detection rate and F1-score. Furthermore, the proposed approaches are thoroughly compared with the existing state-of-the-art studies. [ABSTRACT FROM AUTHOR]

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems. [ABSTRACT FROM AUTHOR]

The emergence of the highly contagious coronavirus disease has led to multiple pandemic waves, resulting in a significant number of hospitalizations and fatalities. Even outside of hospitals, general practitioners have faced serious challenges, stretching their resources and putting themselves at risk of infection. Telemonitoring systems based on Internet of things technology have emerged as valuable tools for remotely monitoring disease progression, facilitating rapid intervention, and reducing the risk of hospitalization and mortality. They allow for personalized monitoring strategies and tailored treatment plans, which are crucial for improving health outcomes. However, determining the appropriate monitoring intensity remains the responsibility of physicians, which poses challenges and impacts their workload, and thus, can hinder timely responses. To address these challenges, this paper proposes an expert system designed to recommend and adjust the monitoring intensity for COVID-19 patients receiving home treatment based on their medical history, vital signs, and reported symptoms. The system underwent initial validation using real-world cases, demonstrating a favorable performance (F1-score of 0.85). Subsequently, once integrated with an Internet of Things telemonitoring system, a clinical trial will assess the system's reliability in creating telemonitoring plans comparable with those of medics, evaluate its effectiveness in reducing medic–patient interactions or hospitalizations, and gauge patient satisfaction and safety. [ABSTRACT FROM AUTHOR]

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems.

Abstract Background Biotechnological syndromes refer to the illnesses that arise at the intersection of human physiology and digital technology. Now that we experience health and illness through so much technology (e.g. wearables, telemedicine, implanted devices), the medium is redefining our expression of symptoms, the observable signs of pathology and the range of diseases that may occur. Here, we systematically review all case reports describing illnesses related to digital technology in the past ten years, in order to identify novel biotechnological syndromes, map out new causal pathways of disease, and identify gaps in care that have disadvantaged a community of patients suffering from these digital complaints. Methods PubMed, MEDLINE, Scopus, Cochrane Library and Web of Science were searched for case reports and case series that described patient cases involving biotechnological syndromes from 01/01/2012 to 01/02/2022. For inclusion the technology had to play a causative role in the disease process and had to be digital (as opposed to simple electronic). Results Our search returned 7742 articles, 1373 duplicates were removed, 671 met the criteria for full review and 372 were included in the results. Results were categorised by specialty, demonstrating that syndromes were most common in Cardiology (n = 162), Microbiology and Infectious Diseases (n = 36), and Emergency and Trauma (n = 26). Discussion The 372 unique patient cases demonstrated a range of severity from mild (e.g., injuries related to Pokemon Go) to moderate (e.g. pacemaker-generated rib fractures) and severe (e.g. ventilator software bugs causing cardiac arrest). Syndromes resulted from both consumer technology (e.g. gaming addictions) and medical technologies (e.g. errors in spinal stimulators). Cases occurred at both the individual level (e.g. faulty insulin pumps) and at the population level (e.g. harm from healthcare cyberattacks). Limitations This was a retrospective systematic review of heterogeneous reports, written in English, which may only reflect a small proportion of true prevalence rates in the population.

The Internet of Medical Things (IoMT) bolstered the smart health care industry in present times by enabling quicker patient monitoring and disease diagnosis. However, there have been problems that need to be resolved using Artificial Intelligence (AI) methods. The major goal of this endeavor is to develop an IoMT-based data fusion system for multi-sensor smart healthcare network. To do this, a new optimization and deep learning approaches are being used in this work. In this research work, a unique smart healthcare framework, Tasmanian and Lichtenberg Optimized Attention Deep Convolution (TasLA) is developed for IoMT systems. This system uses an intelligent data fusion algorithms for collecting of medical data and the diagnosis of disorders. Here, data pretreatment and normalization processes are carried out in order to provide a dataset with balanced attribute information. The qualities or characteristics that will aid in classification are then selected using the most modern Tasmanian Devil Optimization (TDO) approach. The Attention Deep Convolution Classification (ADCC) algorithm is also used to classify the medical condition, thereby improving classification precision and reducing false predictions. To optimally compute the loss function during prediction, the Lichtenberg Optimization (LO) technique is employed to enhance classification performance. The effectiveness and results of the proposed TasLA model are validated and contrasted using various benchmark datasets such as Hungarian, Cleveland, Echocardiogram, and Z-Alizadeh.

The healthcare sector is witnessing rapid transformation with the rise of the Internet of Medical Things (IoMT). This presents unpar-alleled opportunities for continuous, personalized health surveillance. To truly tap into the IoMT's capabilities, it's essential to employ a flexible and robust data processing framework. In this article, we introduce a comprehensive four-tiered architecture tailored for the IoMT. This model, which we foresee as a benchmark for similar platforms, spans from interconnected devices to an edge computing layer, extends through a fog computing level, and culminates in the cloud. To bolster the system's resilience and features, two cross-sectional layers - one centered on security, and the other on artificial intelligence (AI) - are integrated across the four tiers. Additionally, we outline strategies for efficient load balancing, enhancing overall system performance. This initiative marks a pivotal advancement in IoMT architectural standardization, setting the stage for broader, more effective deployment in healthcare. [ABSTRACT FROM AUTHOR]

Background: Biotechnological syndromes refer to the illnesses that arise at the intersection of human physiology and digital technology. Now that we experience health and illness through so much technology (e.g. wearables, telemedicine, implanted devices), the medium is redefining our expression of symptoms, the observable signs of pathology and the range of diseases that may occur. Here, we systematically review all case reports describing illnesses related to digital technology in the past ten years, in order to identify novel biotechnological syndromes, map out new causal pathways of disease, and identify gaps in care that have disadvantaged a community of patients suffering from these digital complaints. Methods: PubMed, MEDLINE, Scopus, Cochrane Library and Web of Science were searched for case reports and case series that described patient cases involving biotechnological syndromes from 01/01/2012 to 01/02/2022. For inclusion the technology had to play a causative role in the disease process and had to be digital (as opposed to simple electronic). Results: Our search returned 7742 articles, 1373 duplicates were removed, 671 met the criteria for full review and 372 were included in the results. Results were categorised by specialty, demonstrating that syndromes were most common in Cardiology (n = 162), Microbiology and Infectious Diseases (n = 36), and Emergency and Trauma (n = 26). Discussion: The 372 unique patient cases demonstrated a range of severity from mild (e.g., injuries related to Pokemon Go) to moderate (e.g. pacemaker-generated rib fractures) and severe (e.g. ventilator software bugs causing cardiac arrest). Syndromes resulted from both consumer technology (e.g. gaming addictions) and medical technologies (e.g. errors in spinal stimulators). Cases occurred at both the individual level (e.g. faulty insulin pumps) and at the population level (e.g. harm from healthcare cyberattacks). Limitations: This was a retrospective systematic review of heterogeneous reports, written in English, which may only reflect a small proportion of true prevalence rates in the population. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) bolstered the smart health care industry in present times by enabling quicker patient monitoring and disease diagnosis. However, there have been problems that need to be resolved using Artificial Intelligence (AI) methods. The major goal of this endeavor is to develop an IoMT-based data fusion system for multi-sensor smart healthcare network. To do this, a new optimization and deep learning approaches are being used in this work. In this research work, a unique smart healthcare framework, Tasmanian and Lichtenberg Optimized Attention Deep Convolution (TasLA) is developed for IoMT systems. This system uses an intelligent data fusion algorithms for collecting of medical data and the diagnosis of disorders. Here, data pretreatment and normalization processes are carried out in order to provide a dataset with balanced attribute information. The qualities or characteristics that will aid in classification are then selected using the most modern Tasmanian Devil Optimization (TDO) approach. The Attention Deep Convolution Classification (ADCC) algorithm is also used to classify the medical condition, thereby improving classification precision and reducing false predictions. To optimally compute the loss function during prediction, the Lichtenberg Optimization (LO) technique is employed to enhance classification performance. The effectiveness and results of the proposed TasLA model are validated and contrasted using various benchmark datasets such as Hungarian, Cleveland, Echocardiogram, and Z-Alizadeh. [ABSTRACT FROM AUTHOR]

Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is very high, which leads to less accuracy of various traditional, parametric, and deep learning-based methods. Hence, as a solution to the challenge of accurate segmentation, we propose an advanced generative deep learning model called the Conditional Generative Adversarial Network (cGAN) for lesion segmentation. In the suggested technique, the generation of segmented images is conditional on dermoscopic images of skin lesions to generate accurate segmentation. We assessed the proposed model using three distinct datasets including DermQuest, DermIS, and ISCI2016, and attained optimal segmentation results of 99%, 97%, and 95% performance accuracy, respectively. [ABSTRACT FROM AUTHOR]

In the new era, the trend of using wearable devices and smart accessories gained considerable popularity and become a necessity utility for human life due to their major role to keep monitoring health conditions and providing healthcare services. The combination of IoT networks with edge computing paradigms develops an intelligent e-health system that aims to monitor different real-time scenarios. The deployment of an e-health system exposes several challenges regarding the security and privacy aspects, particularly in the case of dealing with an enormous quantity of medical data and the risk presented by exchanging operations with external entities. In this paper a comprehensive presentation covered the basic topics of e-health system layers thus the advantages and limitations in terms of existing challenges has been mentioned, subsequently, adapted to the exposed cyber risk through the traditional systems in exchanging medical data, a discussion of the blockchain technology come over for new application opportunities, where this approach efficiently ensure the security of data transactions over the network, in addition, an overview outlined the main research works related to this technology. Therefore, a presentation study of diverse works reveals different security framework solutions related to e-health system's layers, furthermore, uncovering the benefits of integrating intelligent technologies such as Machine Learning (supervised, and unsupervised types), Deep Learning, and Reinforcement Learning as well as introducing a comparison analysis of multiple AI algorithm models based on their efficiency for future deployment related security purposes to provide a smart healthcare monitoring system that meets patient needs. The end of this review highlighted further research directions and the actual open challenges regarding the e-Health system's limitations. [ABSTRACT FROM AUTHOR]

The relationship between computing and healthcare has a long history, but adoption of telemedicine is gradual due to political resistance, lack of infrastructure development frameworks, and lack of resources. One of the most rapid technological advancements will be the Internet of Medical Things (IoMT), which is predicted to bring about the greatest technological delivery ever. Edge computing in conjunction with 5G speed is the solution to achieve the requirements of quality of service metrics metrics during the analysis of clinical data. Artificial intelligence with edge computing has made significant contributions to the smart healthcare system's network for ultra-reliable communication in the areas of less delay, widespread device connectivity, and enhanced speed of data transmission. Since the edge-enabled IoMT-based system in the healthcare system offers a number of extraordinary potential, this Special Issue explores those areas of applicability. The aim of Special Issue is to cover the research difficulties associated with the implementation of edge computing-based IoMT systems in the healthcare system and suggests a framework for such a system. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has shown significant advancements across several industries, including healthcare, using better fusion methodologies, improved data accessibility, and heightened processing capabilities. Although deep learning algorithms excel in challenging scenarios, their widespread adoption presents a challenge due to their need for more transparency. The advent of Explainable AI (XAI) has addressed this limitation by offering AI-driven systems with attributes such as transparency, interpretability, and reliability. The need for transparency in critical sectors like healthcare has prompted an increased academic focus on investigating and understanding these frameworks. The present paper thoroughly examines the latest research and advancements in Explainable Artificial Intelligence (XAI), specifically focusing on its integration into the Internet of Medical Things (IoMT) in healthcare settings. The last section tackles outstanding issues concerning XAI and IoMT and outlines prospective directions for further research. A thorough investigation was carried out across many scholarly databases and online digital libraries, such as IEEE Xplore, ACM Digital Library, Wiley Interscience, Taylor and Francis, ScienceDirect, Scopus, Springer, Google Scholar, Citeseer Library, Semantic Scholar, and other relevant sources. An analysis was conducted on articles published from March 2004 to February 2024, specifically focusing on AI models that explain different healthcare issues. The search query included "Explainable AI" in conjunction with "Open Black Box, Healthcare, Transparent Models, Interpretable Deep Learning, Machine Learning, Medical Information System, Accountability, Smart Healthcare, and Responsible AI", indicating the field's dynamic nature. The authors also examined various techniques and datasets to elucidate healthcare difficulties, including incorporating the Internet of Medical Things (IoMT). The evaluation included more than 105 published models that used clinical data input to diagnose diverse disorders, focusing on incorporating Internet of Medical Things (IoMT) components. In addition to illness diagnosis, these models also elucidate the decisions made. The models were classified according to the input data, used methodologies, and integration of the Internet of Medical Things (IoMT). The categorization included machine learning and deep learning methodologies, particularly on explainability models connected to the Internet of Medical Things (IoMT). These models were classified as model-agnostic, model-specific, global, local, ante-hoc, and post-hoc. This extensive study examines machine learning, and deep learning models developed based on clinical data and associated with the Internet of Medical Things (IoMT) for illness detection. These models improve efficiency and accuracy and provide essential assistance to medical personnel. Explaining each choice made by individuals contributes to the acceleration of sickness detection, resulting in decreased medical expenses for patients and healthcare systems. The integration of Explainable Artificial Intelligence (XAI) and the Internet of Medical Things (IoMT) highlights a fundamental change in healthcare towards more transparent and linked systems, leading to significant breakthroughs in medical decision-making. • Advancing IoMT fusion with Explainable AI for trustworthy healthcare solutions. • A comprehensive analysis of Explainable AI literature in various application areas. • Exploring the transparency of Explainable AI in healthcare solutions. • Identifying Explainable AI's future with IoMT for better healthcare solutions. • Bridging opportunities and challenges in healthcare with IoMT fusion. [ABSTRACT FROM AUTHOR]

In the Internet of Medical Things (IoMT), the Internet of Things (IoT) is integrated with medical devices, enabling improved patient comfort, cost-effective medical solutions, quick hospital treatments, and even more personalized healthcare. The paper first provides the introduction of IoMTs and then introduces an architecture of IoMTs. Later, it provides the current operations of the healthcare system and discusses the mapping of these operations into the architectural diagram. Further, several emerging technologies such as Physically Unclonable Functions (PUF), Blockchain, Artificial Intelligence (AI), and Software-Defined Networking (SDN) are envisioned as important technologies to overcome several challenges in e-healthcare such as security, privacy, accuracy, and performance. Finally, we provide three case studies for IoMT based on – (1) PUF-based Authentication, (2) AI-enabled SDN Assisted e-healthcare, and (3) Blockchain Assisted Patient Centric System. The solutions presented in this paper may have a huge impact on the speed at which IoMT infrastructure can efficiently evolve with market evolution. [ABSTRACT FROM AUTHOR]

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen–antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.

• To mitigate the impact of this multifaceted problem, comprehensive preventive strategies, early detection through regular medical check-ups, and effective management are required. To promote heart-healthy habits and reduce the prevalence of this widespread health concern, not only individual awareness and behavioral changes are required, but also systemic efforts in healthcare policy, education, and accessible medical services. • The IoMT is an important component because it enables seamless gathering of information from a wide range of healthcare devices, like wearables as well as sensors. diseases (CVDs). The approach uses advanced techniques, specifically TabNet in conjunction with catBoost, to improve the accuracy and efficiency of heart disease prediction. The IoMT is an important component because it enables seamless gathering of information from a wide range of healthcare devices, like wearable's as well as sensors. • The research envisions the implementation of this integrated model as a transformative tool in healthcare. By combining the real-time data acquisition capabilities of IoMT with the predictive prowess of TabNet and catBoost, the model aims to provide timely and accurate predictions of heart disease risks. This innovative approach holds great promise for ushering in a new era of personalized and effective preventive care strategies, ultimately leading to improved patient outcomes in cardiovascular health. Heart disease is a widespread health problem that poses a significant global challenge. It includes a variety of heart-related conditions such as heart failure, coronary artery disease, and ventricular fibrillation all of which contribute to a significant global morbidity and mortality burden. Poor diet, physical inactivity, smoking, and underlying health conditions such as hypertension and diabetes. To mitigate the impact of this multifaceted problem, comprehensive preventive strategies, early detection through regular medical check-ups, and effective management are required. To promote heart-healthy habits and reduce the prevalence of this widespread health concern, not only individual awareness and behavioral changes are required, but also systemic efforts in healthcare policy, education, and accessible medical services. This study presents a novel coronary artery disease Forecast Model that seamlessly combines the Internet of Medical Things (IoMT) and Artificial Intelligence (AI) in response to the growing global burden of cardiovascular the approach uses advanced techniques, specifically TabNet in conjunction with catBoost, to improve the accuracy and efficiency of heart disease prediction. The IoMT is an important component because it enables seamless gathering of information from a wide range of healthcare devices, like wearables as well as sensors. diseases (CVDs). The approach uses advanced techniques, specifically TabNet in conjunction with catBoost, to improve the accuracy and efficiency of heart disease prediction. The IoMT is an important component because it enables seamless gathering of information from a wide range of healthcare devices, like wearable's as well as sensors. Proposed model employs Internet of Medical Things (IoMT) to collect a wide range of physiological data from connected medical devices, such as continuous heart rate monitoring, electrocardiograms (ECG), and blood pressure readings. TabNet and catBoost Artificial Intelligence (AI) algorithms are critical in processing and interpreting the complex data obtained through IoMT. TabNet, which is well-known for its ability to efficiently handle tabular data, is used for feature selection and extraction. CatBoost, a powerful gradient boosting algorithm, on the other hand, contributes to the model's predictive accuracy by handling categorical features and mitigating overfitting. The combination of these cutting-edge techniques improves precision as well as interpretability, allowing for a more detailed understanding of the factors influencing predictive outcomes. A large dataset with a variety of patient profiles, risk factors, and medical histories is used in the model's training regimen. By fine-tuning this diverse dataset, the model's adaptability to different demographic and clinical scenarios is improved, paving the way for personalized heart disease risk assessments. Furthermore, the proposed model supports immediate processing, allowing for rapid predictions based on streaming IoMT data. Finally, Heart Disease Prediction Model represents a paradigm shift in predictive healthcare by combining the strengths of IoMT and advanced computer vision techniques. The research envisions the implementation of this integrated model as a transformative tool in healthcare. By combining the real-time data acquisition capabilities of IoMT with the predictive prowess of TabNet and catBoost, the model aims to provide timely and accurate predictions of heart disease risks. This innovative approach holds great promise for ushering in a new era of personalized and effective preventive care strategies, ultimately leading to improved patient outcomes in cardiovascular health. [ABSTRACT FROM AUTHOR]

The epidemic of coronavirus disease which is called COVID-19 is dispersing all over the world in a few months. The traditional health care systems face new challenges associated with the constant increase of infected persons with this disease. In the current time, the integration of smart medical sensors and devices of the Internet of Things (IoT) proposed the new health care domain which is called the Internet of Medical Things (IoMT). With this technology, accuracy, reliability, efficiency, and effectiveness are improved in the health domain. With the combination of cloud computing with IoT architecture, the smart healthcare system is designed which is ableto support real-time applications when Artificial Intelligence (AI) processing on a vast data which is collected by wearable sensors network. The emergence of IoMT has initiated the vital change in assisting the management of diseases, improving the diagnosis of disease, treatment methods and also reducing the cost and errors in healthcare. The implementations of this technology in the health domain not only improve the healthcare quality for patients but also help the healthcare workers. This paper studies the use of IoT solutions throughout this pandemic period in a health care system, starting from early health monitoring solutions from wearable sensors up to a final recovery from COVID-19. IoMT can play an imperative role during this pandemic time and facilitate to stop the spread of the virus, recognize the patient who has been infected with this virus and also help to control the coronavirus infection in real-time. [ABSTRACT FROM AUTHOR]

• A review of COVID-19 guidelines ensures the need of disruptive technologies. • COVID-19 enforces healthcare system to find alternatives for patients' treatment. • The disruptive technologies used to analyze and to restrict the spread of COVID-19. • The study aids healthcare team to detect the plan of treatment remotely and safely. • The analysis of COVID-19 patients ensures the importance of intelligent framework. This paper describes a framework using disruptive technologies for COVID-19 analysis. Disruptive technologies include high-tech and emerging technologies such as AI, industry 4.0, IoT, Internet of Medical Things (IoMT), big data, virtual reality (VR), Drone technology, and Autonomous Robots, 5 G, and blockchain to offer digital transformation, research and development and service delivery. Disruptive technologies are essential for Industry 4.0 development, which can be applied to many disciplines. In this paper, we present a framework that uses disruptive technologies for COVID-19 analysis. The proposed framework restricts the spread of COVID-19 outbreaks, ensures the safety of the healthcare teams and maintains patients' physical and psychological healthcare conditions. The framework is designed to deal with the severe shortage of PPE for the medical team, reduce the massive pressure on hospitals, and track recovered patients to treat COVID-19 patients with plasma. The study provides oversight for governments on how to adopt technologies to reduce the impact of unprecedented outbreaks for COVID-19. Our work illustrates an empirical case study on the analysis of real COVID-19 patients and shows the importance of the proposed intelligent framework to limit the current outbreaks for COVID-19. The aim is to help the healthcare team make rapid decisions to treat COVID-19 patients in hospitals, home quarantine, or identifying and treating patients with typical cold or flu. [ABSTRACT FROM AUTHOR]

The expanding utilization of edge consumer electronic (ECE) components and other innovations allows medical devices to communicate with one another to distribute sensitive clinical information. This information is used by health care authorities, specialists and emergency clinics to offer enhanced medication and help. The security of client data is a major concern, since modification of data by hackers can be life-threatening. Therefore, we have developed a privacy preservation approach to protect the wearable sensor data gathered from wearable medical devices by means of an anomaly detection strategy using artificial intelligence combined with a novel dynamic attribute-based re-encryption (DABRE) method. Anomaly detection is accomplished through a modified artificial neural network (MANN) based on a gray wolf optimization (GWO) technique, where the training speed and classification accuracy are improved. Once the anomaly data are removed, the data are stored in the cloud, secured through the proposed DABRE approach for future use by doctors. Furthermore, in the proposed DABRE method, the biometric attributes, chosen dynamically, are considered for encryption. Moreover, if the user wishes, the data can be modified to be unrecoverable by re-encryption with the true attributes in the cloud. A detailed experimental analysis takes place to verify the superior performance of the proposed method. From the experimental results, it is evident that the proposed GWO–MANN model attained a maximum average detection rate (DR) of 95.818% and an accuracy of 95.092%. In addition, the DABRE method required a minimum average encryption time of 95.63 s and a decryption time of 108.7 s, respectively. [ABSTRACT FROM AUTHOR]