Special Issues Now Accepting Submissions
Submission Deadline: 15 September 2022
Submission Deadline: 31 December 2022
Submission Deadline: 15 November 2022
Machine learning methods previously used to develop rational decisions are presently a demand for Emergency Machine learning. With rapidly expanding datasets, reliability also remains a vital consideration when expanding and validating Machine learning models. Machine learning can also help healthcare institutions meet growing pharmaceutical demands, make or become better deals and lower costs. Machine learning modernisation at the bedside can help healthcare specialists discover and treat disease expertly and accompany more accuracy and personalised care. An observation of fashionable machine learning healthcare reveals how automation change can lead to active, comprehensive care strategies that improve patient results. While researchers in the field of Machine learning and machine intelligence order can be tested to comprehend subgroups of patients, guide scientific administration, and improve two together movement- and patient-centred results. This context features the benefits of these tools realised at various clinical sites and specify how the use of Medical learning, when faithfully built, grants permission to improve during the COVID-19 pandemic. Because of these changes, a predicting model accompanying good early performance concedes the possibility of decline by way of change in the middle from two points patients incapacitate for three weeks distinguished to inferior a week. Also, an individual’s patient portrait concedes the possibility of being in possession evolved entirely from a place where it’s admittance to those time points later all along hospitalisation. Discharges per period increased accompanying the pandemic’s peak and decreased accompanying lowered numbers of new cases.
Submission Deadline: 31 March 2023
Within the computational biology and bioinformatics research communities, conventional analysis strategies lack the strong potential to analyze big data and to extract valuable knowledge from them, leading to incorrect practices. In the recent years, biological data analysis has experienced exponential growth by the application of advanced artificial intelligence (AI) technologies. This is mainly due the high ability and potentials of AI-based systems to develop algorithms and analytical models for interpreting biological information and thus to assist in making accurate predictions and/or decisions. The goal of this special issue is to provide researchers around the globe with cutting-edge research work for the best utilization of AI tools in computational biology and bioinformatics research. Attractive, recent, and innovative AI-based research work for emerging problems in the field of computational biology and relevant problems from the life sciences are invited. Full-length papers of original and unpublished research work as well as review manuscripts related to the Ai applications for the understanding, visualizing, and interpreting biomedical data for biology are welcomed.
Submission Deadline: 30 November 2022
The Neuroimaging system has been driving the CT market for clinical resources for years. Artificial Intelligence has been involved in cutting-edge technologies to get an accurate result despite all the frontiers in MedTech, there are also many other tools that help surgeons for identifying the issue in the applications of radiology. Cutting-edge is an extension of cloud computing in the data manipulation process. It takes special care on some particular segments called reliability, security and real-time prediction. Edge computing is a distributed architecture that is built on the basis of the cloud with the infrastructure of blockchain nodes to store and monitor the transactions provided. This tends to change based on the variability of the epidemic of time for different users. This Special Issue reviews that in these current technologies it is always recommended to train the neural network to get the accuracy in the cutting-edge technologies for More complex and efficient methods that can be always applied into these algorithms to gain the maximum accuracy in the field of neurology.
Submission Deadline: 31 August 2023
Smart healthcare is a viewpoint in which ICT-assisted skills are communicated with handlers in all aspects, including society. Healthcare industry-leading scientific novelties offer peace of mind to senior living residents, staff, and families. To sustain healthcare novelties, they should permit the three mainstays of sustainability that are social, financial, and environmental sustainability. Smart healthcare has been a field of implausible revolution and evolution in the newfangled era of smart societies. It is amongst the most significant constituents of smart cities and societies. More than 50 billion health-associated medical things are expected to be wired this year, and the Internet of Health Things (IoHT) standard will increase with 5G. The IoMT comprises a network associated with various kinds of health devices and sensors including but not limited to blood pressure, image digitizer, oxygen monitor devices, vital sign sensing devices, modems, display, storage devices, and networks of communication. Traditional IoMT comprises of devised used to aggregate data from the patients. However, still, the room is left to efficiently predict the future of the diseases using artificial intelligence (AI), e.g., early detection of COVID-19, pneumonia, arrhythmia classifications, etc. The advancement of IoMT using AI-based public medical care devices is a challenging task. Also, various challenging is available in planning and creating wise medical services devices; there is a prompt need to resolve this issue by proposing inventive public medical services systems and brilliant public medical care devices which can offer calculation and handling of clinical sensor information, move the expected undertakings at the edge level, and fulfill the current needs of savvy medical services arrangements.
Submission Deadline: 1 February 2023
The core technologies of Metaverse, such as VR/AR and AI, have broad application prospects in the medical field. Picture Archiving and Communication System (PACS) medical imaging system is a bridge between data and technologies such as AI in the Metaverse. In terms of disease visualization, CT, DR, nuclear magnetic resonance and other imaging devices aim at disease visualization. Because organs are invisible inside the human body, the goal of the first generation of medical devices is to visualize invisible things. For example, the lung nodule positioning and three-dimensional visualization technology can take internal slice images one by one through medical devices. As a result, the “invisible” human organs can “show their original shape”, and even build a holographic digital human, providing a reference for surgical planning. The ultimate goal of Metaverse is to provide data basis for comprehensively improving human health by monitoring higher-level vital signs indicators such as human microorganism, nutrition and psychology, thus finding biological targets for health intervention.
Submission Deadline: 15 December 2022
Psychophysiological computing focuses on the quantification, fusion, analysis and mining of multi-source physiological data (such as brain imaging, EEG, body electricity, heart rate, respiration, body temperature, etc.) to reversely derive the complex physiological-psychological mapping relationship and achieve a more comprehensive and objective quantitative perception and reasoning calculation of different mental states. One of the key enablers for Psychophysiological computing is the Internet of Things (IoT), which can exploit state-of-the-art communication technologies to support advanced services. Meanwhile, Artificial Intelligence (AI) has recently emerged as a powerful weapon that supports very implement efficient data analysis and make accurate decisions on service provisions in various kinds. Combining IoT with advanced AI technology can greatly benefit Psychophysiological computing.
Submission Deadline: 31 December, 2022
Most of the AI-based healthcare applications are prediction techniques, which employ amounts of healthcare-related data for training and are then used for making smart diagnosis of a new input. Such a pattern suffers from the in-sufficient data, the data imbalance, and the biases of the training samples. Large datasets that are diverse and representative are in high demand for improving the robustness. The generative AI (GenAI) paradigm is a promising solution. The GenAI refers to AI techniques that enable using existing content like text or images to create new contents. It is able to abstract the deep dependencies and distributions in the real data sets, and ensures novel and higher-quality outputs by self-learning rather than a replication, while also preserves the patient privacy. Primitive attempts have been done to explore GenAI for promoting the AI-based smart healthcare, however, huge potentials are waiting to be explored and some emerging challenges need to be addressed, such as the problems of cross-model data synthesis, evaluation metrics of the synthetic data, smart healthcare applications, information security, and so on.
Submission Deadline: 30 September, 2023
With the development of smart healthcare systems, the privacy and security of biomedical data have become an urgent problem to be solved. Biomedical data is faced with data leakage and data tampering in all links of collection, processing, and transmission. It has a great negative impact on personal reputation, personal privacy, and public opinion. The development of XAI can produce more interpretable methods, improve the prediction accuracy of the model, and enable users to understand, trust and make effective use of artificial intelligence. This special issue focuses on the data quality, privacy, security, and application management of biomedical data based on XAI in intelligent healthcare systems, aiming to promote the development of relevant fields and provide a safe environment for the next generation of smart healthcare.
Submission Deadline: 30 September, 2022
massive e-Health digital data. It provides Data Stream configuration and provisioning to facilitate data federation and aggregation over streaming paths, application flow associations, and e-Health message segmentation during processing. The Big data analysis of EMRs, EHRs, and other medical information is constantly aiding in enhancing more robust prognostic schemas.
Submission Deadline: 31 August, 2023
This Special Issue is created with an interdisciplinary approach, involving topics that cover (i) the main features in the field of cardiovascular sensors, and (ii) biomedical engineering analysis of this data for cardiac diagnostics and prognosis. We hope the high-quality research in this important field will have an impact on future disease management, and also hope to bring together a collection of both original research and review papers that cover modern technologies in all aspects of cardiovascular illnesses and heart attack prevention by using sensors and equipment. The special issue devoted to this topic will make significant contributions to the rapidly growing field of novel and innovative smart sensors for cardiovascular disorders and heart attack prevention. It will have a positive impact on the domain knowledge and practices for improving people’s quality of life.
Submission Deadline: 31 October, 2022
Artificial intelligence (AI) is a revolutionary technology that enables computational approaches to examine complex information. Diagnostic biomedical imaging is the most potential clinical implementation of AI, and increasing effort has been paid to develop and perfect its services to identify better and measure a range of clinical problems. AI-assisted diagnostic research has seen incredible precision, tolerance, and selectivity to identify minor radiological defects, potentially improving global health. Medical experts and clinicians can utilise AI to help them diagnose a wide range of illnesses using biomedical imaging.
The biomedical sectors identified the possible uncertainties of this innovation at the start of the revolution. Biomedical imaging observations are among the most comprehensive and sophisticated data regarding individual patients. The need for AI in biomedical imaging is now being studied in depth. AI has shown excellent reliability and selectivity in discovering imaging disorders; It can enhance surface diagnosis and screening. It can also use AI to detect enlargement of particular muscle tissues, including the left ventricular membrane, and track changes in blood volume and flow through the cardiac and linked vessels. The systems and materials of AI’s possible benefits in the ecosystem of its special competence will be required. It will be difficult to draw the boundary between reliable diagnosis imaging and overtreatment. To enhance the performance and applicability of AI experiments, continuous employment of out-of-sample validation and well-defined subgroups are essential. AI could provide new ways to learn more perfect imaging alterations representing incompletely known illnesses.
Submission Deadline: 1 April, 2023
Pervasive computing has revolutionized how we collect data and interact with information. Research interest in pervasive computing has been growing exponentially over the years, demonstrating enormous potential in biomedical applications ranging from a research-fertile field to clinical translation and healthcare delivery. The sophisticated capabilities of smartphones integrating diverse sensors along with wearable and non-wearable sensors provide the opportunity to collect longitudinal, multimodal data streams and facilitate near real-time monitoring. Moreover, these devices are becoming increasingly affordable and have already been embraced by many people, thus enabling large scale investigations and clinical trials. The collected data streams from these ubiquitous devices, coupled with emerging advances in data science and machine learning which enable fast and advanced processing of the collected datasets, lead to unprecedented opportunities for transforming healthcare. Along with these opportunities, there are new practical and social challenges, including data privacy, empowering individuals make decisions about their health trajectories, considerations regarding widening the gap with health inequalities, implementations at scale, and liability for the application and monitoring of data insights and outputs that can be extracted.
Submission Deadline: 30 June, 2022
Generally, big data in healthcare applies to large, complex e-health datasets that seem challenging to handle with typical data management hardware, software, and procedures. Clinical details of doctors, their prescriptions, laboratory data, CT pictures, insurance files, MRI images, and other data associated with administrative applications, EPR data, drugstore documents, and soon are all types of big data healthcare. The development and application of ML technologies promote the efficient use of Big Data in e-Healthcare. ML and AI approaches are sometimes used interchangeably. Allowing remote electronic accessibility and easy data processing, EHRs make patient data more available to patients, providers, and researchers. Incorporation of EHRs with diagnostic tests like genomic sequencing, MRIs, etc., gives ample platform for Big Data because it aids doctors to know the genetic reasons of cancers better and thus promotes more adequate treatment procedures and enhance screening and prevention measures. Oncology treatment, in particular, shows how Big Data can directly help patient care.
Submission Deadline: 31 September, 2022
Benefiting from the encouraging results of AI on Big Data, AI for personalised healthcare through Edge-of-Things will pave the way for intelligent health-related applications on edge devices, such as smart sensors and wearable devices. However, the variety and complexity of the data require the provision of new AI models and technologies able to process and analyse them in a trustworthy and collaborative way. In this regard, the characteristics of trust and collaboration in AI systems are highly valuable for applying AI to personalised healthcare services. Trustworthy and collaborative AI is designed to encourage transparent, reliable, and unbiased AI systems and ensure their adequacy to tackle predictive and prescriptive healthcare problems. For this purpose, such AI systems need to be able to understand what’s wrong, figure out how to overcome the resulting problems, involve human intelligence in the discovery process, and then take what they have learnt to overcome those challenges for the future. This special issue intends to facilitate advancements in all state-of-the-art trustworthy and collaborative AI techniques for personalised healthcare, and establish a new era of healthcare systems with AI.
Submission Deadline: 31 October, 2022
Recently, deep learning (DL) has achieved outstanding performance in academic and industrial research, and become a vital utensil in a wide range of medical image computing tasks, including cancer detection, tumor segmentation, tumor classification, vessel segmentation, and cancer prediction. But, it is still not clear how information delivered through DL models, and how DL models work to a rapid, safe and robust prediction. Hence, experts/users wanted to know interpretation of DL model rather than black box nature, and the latest research advances of interpretable deep learning (IDL). This critical research topic will bring new challenges and opportunities to the new age AI community. The purpose of this special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of interpretable deep learning, to solve the problems of automatic analysis of medical image.
Submission Deadline: 30 September, 2022
Cancer is a complex and heterogeneous disease which often leads to misdiagnosis and ineffective treatment strategies. Many mathematical and computational approaches have been implemented in basic cancer research and clinical diagnosis/therapy over the past few decades. Motivated by rigorous mathematical theory and biological mechanisms, the advanced computational methods for cancer data analysis are robust and clinically practicable, which will be strong interpretability combining clinical data and algorithms in an era of artificial intelligence. Moreover, these methods also allow a deeper exploration of cancer from the perspective of computational science, such as the mapping of biological and computational correlations among multiple omics data at various scales and views. The multimodal cancer data include but are not limited to radiographic, pathology, genomics, and proteomics. The goal of this Special Issue is to publish the latest research advancements in theoretical, computational, and applied aspects of computational mathematics in cancer data analysis for cancer research and clinical diagnosis/therapy.
This Special Issue is in cooperation with the 1st WORKSHOP on Computational Mathematics Modeling in Cancer Analysis (CMMCA2022) to be held in conjunction with the MICCAI2022 , along with papers submitted within the open call.
Submission Deadline: 31 August, 2022
“Remote cameras have been used to measure physiological signals from human face and body, thereby eliminating mechanical contact with the skin that are common in wearable sensors. Advancements in biomedical optics, computer vision and AI enabled various camera-based measurements, including vital signs like heart rate, respiration rate, SpO2, blood pressure, and physiological markers/indicators that have diagnostic capabilities. Image and video analysis also permit the measurement of human semantics, context and behaviours that provide new insights into health informatics (e.g. facial analysis for pain or delirium assessment), which is an unique advantage of camera sensors as compared to biomedical sensors. Camera-based health monitoring will bring a rich set of compelling healthcare applications that directly improve upon contact-based monitoring solutions in various scenarios like clinical units (e.g. ICU, NICU) and assisted-living homes (e.g. senior center, confinement center), impacting people’s care experience and quality of life. After years of R&D in this field, it is time to bring the concepts and prototypes (setup and algorithms) from labs to real-world scenarios to demonstrate their actual performance and societal values with concrete use cases like clinical trials and pre-development showcases. This special issue focuses on the latest developments and technologies pertaining to Camera-based Health Monitoring in Real-world Scenarios, specifically on innovation, validation and demonstration in healthcare applications.”
Submission Deadline: 30 September, 2022
AIoPT (Artificial Intelligence of Paediatric Things) enabled Preventive, Assistive, and Medical Children Health Informatics (PAMCHI) aims to address the aforesaid areas as well as analyze current issues in the fields of paediatric training, quality, and informatics. The authors, who are experts in a variety of paediatric settings (hospital medicine, intensive care, emergency medicine, clinical informatics, nursing, and quality improvement research), provide an overview to help practicing pediatricians broaden their knowledge base and apply the content of these articles to their daily practice.
Submission Deadline: 31 August, 2022
The introduction of Digital Twins (DT) is anticipated to radically distrupt clinical practice towards personalized medicine, since it will allow rapid decision making and prognosis at a simulation level, without touching the patient. On the other hand, developing a Digital Twin of a human body is a very demanding and complex process that still remains unrevealed. The wealth of medical data, collected today from both medical (i.e. hospitals, laboratories, etc.) and non-medical (i.e. home, outdoors) environments, opens new roads in the area of AI-powered medicine that needs to be explored Furthermore, digital twins offer a different focus than precision medicine, which was already considered in previous JBHI issues. Specifically, DT look at modeling the individual as a whole, rather than answering questions that rely mainly on the systems biology realm. Consequently, we believe that this special issue will be a significant addition, and it will attract very interesting submissions and the interest of the majority of JBHI readers.
Submission Deadline: 15 February, 2022
The digital revolution has been transforming biomedicine across the whole continuum, from scientific discovery to clinical translation and healthcare delivery. The rapid advances in artificial intelligence coupled with new data generation technologies and new hardware accelerators are influencing the biomedical discovery process, from data-driven hypothesis generation, to design of virtual experiments, to generating surrogate models of complex biological and healthcare systems, to automating healthcare delivery. This Special Issue is related to the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2021).
Submission Deadline: 1 March, 2022
The coordination between embedded microprocessor and wireless transmission is becoming more and more important for telemedicine, especially for medical image lesion detection technology. Using image processing technology to analyze and process two-dimensional slice images can help doctors make qualitative or even qualitative analysis of lesions and other areas of interest, thus significantly improving the accuracy and reliability of medical diagnosis. Embedded medical image lesion detection is advantageous of small volume, low cost, good stability, and strong adaptability. Applied to medical image recognition and diagnosis, it can significantly reduce the burden of doctors on massive and complex medical image data and help doctors diagnose diseases that are difficult to find.
Submission Deadline: 28 February, 2022
The role of Digital Twin in healthcare is changing from evolution to revolution. With a digital twin, a hospital can be virtualized to create a safe environment, which verifies the influences of changes on the performance of the organizational and structural system without risk. It is extremely important in the healthcare sector, as it enables informed strategic decisions to be made in a highly complex and sensitive environment. But digital twin technology can also be used to represent the genome, physiological characteristics and lifestyle of an individual in order to personalize medicine fully. A digital twin of a human body can allow doctors to discover the pathology before the disorders are evident, experiment with treatments and better prepare for surgery. In this special issue, we are looking for Digital Twin innovation also driven by Artificial Intelligence.
Submission Deadline: 15 Jan, 2022
Large amounts of health-related data are produced daily, such as those from personal devices, e.g., fitness trackers or mobile applications, ambient sensors, clinical data in electronic health records, pathology reports, lab results, and medical images, and voice recordings, etc. The practice of modern medicine increasingly relies on data from multiple sources to guide better care. Modern data science, analytics, machine learning, and artificial intelligence-based tools embedded with self-learning mechanisms offer the promise to revolutionize/remodel medicine and patient care. Multimodal learning mechanisms that take advantage of the multitude of data sources are instrumental in realizing that promise. In this special issue, we invite novel research contributions describing tools and techniques that integrate multiple data types to describe a particular medical event/case toward developing higher confidence in their decision-making and guidance. Derivation of meaningful data from an intelligent fusion of medical imagery and electronic health records is an example of multimodal learning.
Submission Deadline: 15 Feb, 2022
The following are the steps for the application of federated learning for Internet of Medical Things
- The medical data of patients generated from IoMT devices like smartphones, wearable devices, ECG monitoring device, etc. are stored on local devices.
- The machine learning models, which are distributed to the clients, as part of the federated model, will be applied on the local devices to understand the patterns from the data.
- Instead of sending the entire raw data to the central server, only model parameters/gradients are sent to the global server, thus preserving the privacy of the medical data by not exposing them to adversaries while transmitting to the global server.
- The parameters/gradients aggregated from all the clients will be trained at the central server for global predictions.
Submission Deadline: 15 Dec, 2021
Wearable, implantable, mobile and remote healthcare applications offer innovative solutions for healthcare problems. This is because sensors associated with mobile and wearable devices monitor everything from patient body movements to the electrical activity of the heart on a daily routine. As a result, the data collected from the wearable and implanted devices becomes indispensable for the advanced research on healthcare informatics to manage illness and improve patient health. The data obtained from these devices have a direct impact on clinical decision making. From a technical perspective, the big data generated from wearable, mobile, and healthcare applications provide both challenges and opportunities for researchers to effectively use it for future healthcare applications. Issues such as data complexity, security, and ethical issues are among the top priority. At the same time, enhanced usability of these data helps to better understand the major public health issues and unaddressed chronic health conditions. This special issue intends to apply for advances in sensor informatics to effectively analyses the plethora of data they generate and effectively use it for public health welfare with enhanced security and ethical regulations.
Submission Deadline: 30 Sep, 2021
With the advent of advanced machine learning techniques, neural networking along with fuzzy logic and other artificial intelligence models evolves as a revolutionary technology in the field of Internet of Things (IoT), Industrial IoT (IIoT), and various smart applications. Nevertheless, the next-generation Internet of Medical Things (Nx-IoMT) arrives as the IoT solutions for smart health and other medical industry applications. Nx-IoMT is made up of various IoMT features along with smart fuzzy-edge and Neuro-edge computing models for human-to-machine and machine-to-human solutions that can be used for remote monitoring and diagnosis with medical guidelines.
Submission Deadline: 20 February, 2022
The existing deep learning models, are less interpretable, i.e., neither provide explanations nor trustworthy for the predictions. Furthermore, several other challenges exist, such as ethical, legal, social, and technological issues of the existing AI. Trustworthy and explainability in AI tools based on Deep Learning (DL) is an emerging field of research with great promise for increased high-quality healthcare. Particularly, it refers to AI/DL tools and techniques that produce human-comprehensible solutions, i.e., provide explanations and interpretations for disease diagnosis and predictions, as well as recommended actions.
Submission Deadline: 15 Nov, 2021
With the release of large public datasets, development of novel learning algorithms and network architectures with open-source implementations, and the availability of powerful and inexpensive graphics processing units, deep learning has become the technique of choice in a wide variety of medical image analysis problems over the past decade. Skin image analysis is no exception, as demonstrated by the large number of deep learning-based contributions/entries submitted to our past five ISIC Workshops/Challenges. The goals of this special issue are to facilitate advancements and knowledge dissemination in deep learning-based skin image analysis, raising awareness and interest for these socially valuable tasks.
Submission Deadline: 30 Nov, 2021
Artificial intelligence (AI) for medical imaging is applied in three domains: pre-DICOM, pre-processing and clinical applications. Clinical applications mainly cover topics such as disease detection, classification, segmentation, registration. Pre-processing components are mainly designed for facilitating applications using image transformation such as image normalization, noise reduction, bias correction in MR. AI in the pre-DICOM domain is expected to improve imaging workflow, image protocol selection, imaging quality, imaging scanning time before images are converted into DICOM format for radiologists to review. The trends of AI publications in medical imaging have been gradually extended from clinical applications to pre-processing and, to pre-DICOM. In this special issue, we are looking for pre-DICOM innovations driven by AI.
Submission Deadline: 15 December, 2021
Cognitive Cyber-Physical Systems (CCPS) are witnessing in rapid transformation as an interdisciplinary technology that blends physical components and computing devices to enable the Artificial Intelligence (AI) based solutions. CCPS will be playing a significant role that integrates machine learning/AI techniques and resulted in dramatic improvements for medical informatics and the future of human-augmentation i.e., Cyber-Physical-Human Medical Systems (CPHMS). CPHMS are coordinating supervisory medical systems and medical resource everywhere; there is a great scope towards health consciousness and healthy society. Medical Cyber-Physical Systems (MCPS) in healthcare towards critical integration in network of medical devices.
Submission Deadline: 30 March, 2022
The rapid advances in the Internet of Things (IoT) and the needs to distributed intelligence and artificial intelligence of things (AIoT) in sensor systems have brought some new challenges of big e-health data with itself where massive data are collected by different medical and healthcare monitoring sensors. The health sensing technologies have become more demandable in IoT-based healthcare systems for a development, testing, and trials such that they should be a part of both clinics and homes to reach the concept of smart monitoring of patients. This special issue wishes to give a deep perception on how to sense, process, and intelligently communicate biomedical data through remote access.
Submission Deadline: 31 August, 2021
The outbreak of different types of heath disease such as COVID-19 is in its growing stage due to the lack of standard diagnosis for the patients. The situation of any populous area in a geographic location is very critical due to the quick virus spread from an infected individual to the rest. Currently, medical administration is at a crisis point due to the rapidly increasing number of cases and limited medical facilities. Thus, it is time to explore and design an intelligent model to monitor patient health symptoms remotely and predict and detect the abnormality of the patient’s health status in quick succession. Thus, the health status of a critical patient can be identified via a well-adjusted predictive model by analyzing the observed parameters of the health. This special issue focus on discussions and insights into the latest advancements and technologies about advanced wearable sensors for smart sensing and disease prediction, specifically on design, theory, modelling, manufacturing, fabrication, data analysis, analytics, and applications of advanced wearable sensors used in disease prediction, forecasting, or monitoring the health symptoms with advanced AI-enabled technologies such as machine learning and deep learning techniques.
Submission Deadline: 31 January, 2022
Researchers in machine learning including those working in computer vision, image processing, biomedical analysis, and related fields when tied with experienced clinicians can play a significant role in understanding
and working on complex medical data which ultimately improves patient care. Developing a novel machine-learning algorithm specific to medical data is a challenge and need of the hour. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information.
Submission Deadline: 30 Sept, 2021
Construction of AI-driven engineering platform is an important research method of synthetic biological system. Through data driven and continuous learning, the deep integration of artificial intelligence and synthetic biology is the general trend, which brings new opportunities for the development of synthetic biology. In this special issue, we are looking for emerging technologies, novel studies, and promising developments, which can realize and elevate the effectiveness and advantages of AI-driven synthetic biology for human wellbeing.
Submission Deadline: 2nd Mar, 2022
Biomedical videos are generated at unprecedented rates. Example applications include diagnostic, surgical and capsule endoscopy, cardiac ultrasound, gait analysis, video microscopy, video angioscopy, and clinical education. In clinical practice, biomedical videos are reviewed by clinical experts and then stored away, not to be observed or utilized again. The resulting large collections of biomedical videos present a unique opportunity for the development of artificial intelligence (AI), machine learning and deep learning diagnostic systems that can be trained and tested on large-scale biomedical video databases. This special issue will address several challenges associated with the development of Computer-Aided Diagnostic Systems that are based on the use of large-scale biomedical video analysis methods. Critical challenges include the development of fast and reliable methods for biomedical video analysis, the effective storage and retrieval of large-scale biomedical video databases, the effective use of multi-scale techniques in space and time, the effective use of biomedical videos in multimodal representations, the development of explainable methods, and high-performance computing methods for processing large-scale video databases.
Submission Deadline: 30 April, 2021
The objective of this special issue is to attract high-quality research and survey articles that promote research and reflect the most recent advances in addressing the security and privacy issues of the medical data for smart healthcare applications. We welcome researchers from both academia and industry to provide their state-of-the-art technologies and ideas covering all aspects of security and privacy solutions for the applications.
Submission Deadline: 30 June, 2021
This special issue focuses on smart sensors challenges in IoMT, and solutions that leverage techniques and insights from the domains of artificial intelligence, edge computing, and IoT. Specifically, it also solicits high quality contributions investigating the usage of biometric signals in the context of IoMT for continuous monitoring for patient-centric healthcare.
Submission Deadline: 1 Aug, 2021
Augmented Reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs, especially in the age of artificial intelligence (AI). AR can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. Bioinformatics-related research produces huge heterogeneous amounts of data. This wealth of information includes data describing metabolic mechanisms and pathways, proteomics, transcriptomics, and metabolomics. In summary, AR is a cool upcoming wave that will be associated with Bioinformatics, where the vast repositories of data will enable an AR lens into the scenarios in ways that provide near-immediate insight at a level of depth unimaginable previously. As a result, this special session aims to bring the latest results over Bioinformatics and Augmented Reality technologies for both academia and industry. It can help technicians to exchange the latest technical progress.
Submission Deadline: 1 Nov, 2021
Due to the proliferation of biomedical imaging modalities such as Photoacoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, Single Photon Emission Computed Tomography (SPECT), Magnetic Resonance (MR) Imaging, Ultrasound, and Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Electron Tomography and Atomic Force Microscopy, massive amounts of biomedical and health informatics data are being generated on a daily basis. How can we utilize such big data to build better health profiles and better predictive models so that we can better diagnose and treat diseases and provide a better life for humans? In the past years, many successful learning methods such as deep learning were proposed to answer this crucial question, which has social, economic, as well as legal implications.
A number of significant problems plague the processing of big biomedical and health informatics data, such as data heterogeneity, data incompleteness, data imbalance, and high dimensionality. What is worse is that many data sets exhibit multiple such problems. A majority of existing learning methods can only deal with homogeneous, complete, class-balanced, and moderate-dimensional data. Therefore, data preprocessing techniques including data representation learning, dimensionality reduction, and missing value imputation should be developed to enhance the applicability of deep learning methods in real-world applications of biomedicine and health informatics.
This special issue aims to provide a diverse, but complementary set of contributions to demonstrate new developments and applications that covers existing above issues in data processing of big biomedical and health informatics data. We would also like to accept successful applications of the new methods, including but not limited to data processing, analysis and knowledge discovery of biomedical and health informatics data.
Submission Deadline: Continuous up to June 30th, 2022
On March 12th 2020, the World Health Organization (WHO) announces COVID-19 (COronaVIrus Disease 2019) outbreak as a pandemic. This global pandemic is caused by a new coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was first discovered in December 2019 in China. Until December 2020, COVID-19 has infected more than 68 millions of people and the reported deaths are more than 1.5 million globally. Seniors and people with suppressed immune system or chronic diseases are at higher risk. Finally, almost 4 billion of people stay at home. Under these extremely urgent circumstances, we have decided to extend the submission deadline for this Special Issue until December 31st, 2021.
This Special Issue aims (1) to encourage the stakeholders relating to COVID-19 to share data source, data harmonization, and tools, which can speed up COVID-19 research for years to come; (2) to inspire new informatics method development for rapid testing of virus in humans; (3) to present advanced informatics solutions that utilize machine learning and artificial intelligence methods such as deep learning to analyze COVID-19 data for diagnosis, treatment, and prognosis; (4) to develop computational models and tools to track virus propagation and recurrence; and (5) to model outbreaks for policy makers for better decision making. Informatics goals include data harmonization, data quality control, multi-modality data integration, advanced analysis pipeline such as deep learning, causal inference, real-time decision making, and interpretable models. Researchers, who are using informatics to address COVID – 19 issues are encouraged to submit high quality data and unpublished work. The submitted manuscripts will be processed through a fast track procedure, and the time from submission to first decision will be limited to 15 days.
Upcoming Special Issues
Submission Deadline: 15 Feb, 2021
Generative adversarial networks (GANs) have received broad interest in computer vision due to their capability for data generation or data translation. Currently, GAN has been rapidly adopted in many applications cross healthcare and biomedicine, addressing problems in image reconstruction, segmentation, classification, and cross-modality synthesis. Despite GAN substantial progress in these areas, their application to medical image computing still faces several challenges. For example, how to synthesize realistic or physically-plausible imagery from small datasets? What are the best GAN architectures and loss functions for specific image computing tasks? When is possible to conduct unsupervised/weak versus supervised deep learning? How to deal with noisy and incomplete data? How to deal with data that is only partially labelled or annotated? How to ensure that learning from GAN-synthesized data generalizes to real-world data? How to develop GAN architectures that integrate biomedical imaging with other biomedical data like omics, radiological text reports, electronic health records, etc.? The goal of this special issue is to attract and highlight the latest developments in GANs for biomedical data processing, and overview the state-of-the-art methods and algorithms at the forefront of using GANs in biomedical image computing.
Submission Deadline: 28 Feb, 2021
Data analytics for Public Health Care has been a pressing challenge for years. Public Health Care organizations must be able to manage, analyze, and interpret data in order to identify the best ways to deliver high quality care. There are a wide range of tools for data analytics in health care, with clinical and operational applications to help organizations capture health data for advancing medical care. Health care data is collected from a variety of systems and devices, such as online patient portals, electronic medical records and health tracking devices. As a result, data exists in different formats, from clinical notes to medical images and at times, the data is unstructured. Data governance covers master data management, which ties master data in a single and reliable source of data to be used for care improvement and patient safety. Data analytics refers to analysis of the data in some way using quantitative and qualitative techniques to be able to explore for trends and patterns in the data. Health data analysts should have the advanced knowledge “to acquire, manage, analyze, interpret, and transform data into accurate, consistent, and timely information”. This special issue aims to consolidate recent advances in data analytics for public health care, research in theory and applications. Pilot studies in analytics-enabled healthcare are especially welcome.
Submission Deadline: 30 May, 2021
As the coronavirus pandemic deepens, lots of people lose their jobs and normal pace of life, resulting in lots of negative emotions, such as nervous, anxious, sleepless and depressed. There is an urgent demand to pay more attention to psychological health for human wellness by providing methods and means of sensing psychological parameters, emotion care and mental disorder patient monitoring, especially during these difficult times. With the aid of wearable computing technology and artificial intelligence, emotion and mental disorder detections are available through sensing and analyzing psychological parameters. Wearable sensors can collect multimodal data, such as physiological data of human body and psychological data closely related to emotion, including ECG, EEG, blood pressure, blood oxygen, etc. Combining with conventional data, such as video, audio and speech text data, significant mental health characteristics can be obtained using deep learning technology. Though sensing psychological parameters and AI-enabled emotion care are expected to play a major role in improving human wellness, it faces a lot of challenges, such as psychological data processing and analysis, AI-based emotion monitoring and care, etc. Therefore, the main objective of this special issue is to presenting and highlighting the advances and latest novel and emergent technologies, implementations, applications concerning the sensing psychological parameters, emotion care and mental disorder patient monitoring.
Submission Deadline: 31 Oct, 2020
There has been tremendous growth in the scale and complexity of biomedical data in the past decade, creating new challenges for analyzing such big data. The focus of this Special Issue will be focused on recent advances in algorithms and analysis tools in biomedical informatics with an emphasis on emerging data types and technologies. It will feature the extended version of some of the best papers selected from ACM BCB 2019. The Special Issue will be open only to invited papers from ACM BCB 2019.
Submission Deadline: 31 Dec, 2020
The Internet of Medical Things (IoMT), which includes medical devices, wearable devices, sensors and apps, is a critical piece of the digital transformation of healthcare, as it allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment and enhanced customer experiences. IoMT can help monitor, inform and notify not only care-givers, but provide healthcare providers with actual data to identify issues before they become critical or to allow for earlier invention. While IoMT offers enormous benefits, the ubiquitously connected devices also pose new challenges. On the one hand, there has been a great improvement in cyberinfrastructure in the era of Industry 4.0, which enables high-frequency long term observational medical data being collected with the help of IoMT. How to convert these data into relevant critical insights that can then be used to provide better care poses a great challenge. On the other hand, although IoMT applications can run well on exiting wireless communication technology, i.e., 4G LTE, there will be others in the future that will require single-digit milliseconds latency and massive bandwidth such as telesurgery. To tackle these challenges, integration AI and 5G into IoMT may achieve an elegant breakthrough in terms of seamless interoperability, low cost, high speed, and low latency, and increased efficiency. Considering the benefit of AI and 5G for IoMT, various AI/5G empowered frameworks/architectures/systems for smart healthcare have been proposed. Even though these approaches have achieved certain success, there exist various scientific and engineering challenges. These open issues call for extensive attention from both academia and industry.
Submission Deadline: 31 Jan, 2021
Nowadays, all over the world, the number of Information and Communication Technology (ICT) investments in health and well-being is rapidly increasing. In this context, there is a growing interest about telehealthcare that allows the provisioning of various kinds of health-related services and applications over the Internet. The main benefits of telehealthcare are: it reduces the risk of infection because patients can make use of remote healthcare services directly in their homes without the need to physically move in clinical centers; it can optimize healthcare workflows; it pushes down clinical costs; it improves the quality of life of both patients and their families. Telehealthcare solutions are typically aimed at tele-nursing, tele-rehabilitation, tele-dialog, tele-monitoring, tele-analysis, tele-pharmacy, tele-trauma care, tele-psychiatry, tele-radiology, tele-pathology, tele-dermatology, tele-dentistry, tele-audiology, tele-ophthalmology, etc. In recent years the rapid advent and evolution of emerging ICT solutions (such as Internet of Things (IoT), Cloud/Edge/Fog computing, Artificial Intelligence (AI), Blockchain, etc.) are revolutionizing the whole telehealthcare sector.
This special issue aims to attract contributions from both academic and industrial organizations focusing on the application of such an emerging ICT for addressing Telehealthcare issues.
Submission Deadline: 24 Aug, 2020
Modern medicine and healthcare have become more complex and less explainable and interpretable than ever. Artificial Intelligence (AI) and AI-based automated recommendations and actions have increased dramatically in every aspect of human life. Reliance on AI to automate disease detection, diagnosis, and prediction, and informed decision-making is also on the rise in all fields of medicine. Explainable AI addresses some of the restrictions of black-box AI systems to explain and interpret their diagnosis, predictions, and recommended actions to stakeholders. It aims to create more understandable, interpretable, and reliable models, by improving the quality of predictions.
Submission Deadline: 31 January, 2021
In recent years, the development of biomedical imaging techniques, integrative sensors, and artificial intelligence, brings many benefits to the protection of health. We can collect, measure, and analyze vast volumes of health-related data using the technologies of computing and networking, leading to tremendous opportunities for the health and biomedical community. Meanwhile, these technologies have also brought new challenges and issues. Biomedical intelligence, especially precision medicine, is considered one of the most promising directions for healthcare development. The practice of biomedical intelligence is based on the prescriptive and predictive analytics of Big data. Biomedical intelligence systems include hardware, computational models, databases, and software that optimize the acquisition, transmission, processing, storage, retrieval, analysis, and interpretation of vast volumes of multi-modal health-related data. Currently, these systems have been deployed in solutions that integrate a variety of technologies, including machine learning (especially deep learning), artificial intelligence, computer vision, Internet of Things, E-Health, bioinformatics, sensors, etc., to achieve patient-centric healthcare. It is expected that the efficiency, accuracy, predictive value, and benefits of biomedical intelligence will greatly improve in the years to come. Researchers from academic fields and industries worldwide are encouraged to submit high quality unpublished original research articles as well as review articles in broad areas relevant to Multi-modal Computing theories and technologies for Biomedical Intelligence Systems.