IEEE Journal of Biomedical and Health Informatics publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences and biomedicine. Papers must contain original content in theoretical analysis, methods, technical development, and/or novel clinical applications of information systems. Topics covered by J-BHI include but are not limited to: acquisition, transmission, storage, retrieval, management, processing and analysis of biomedical and health information; applications of information and communication technologies in the practice of healthcare, public health, patient monitoring, preventive care, early diagnosis of diseases, discovery of new therapies, and patient specific treatment protocols leading to improved outcomes; and the integration of electronic medical and health records, methods of longitudinal data analysis, data mining and discovery tools. Manuscripts may deal with these applications and their integration, such as clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, informatics in biological and physiological systems, personalized and pervasive health technologies (u-, p-, m- and e-Health), telemedicine, home healthcare and wellness management. Topics related to integration include interoperability, protocol-based patient care, evidence-based medicine, and methods of secure patient data.

Indexed in Pubmed® and Medline®, products of the United States National Laboratory of Medicine
   
The articles in this journal are peer reviewed in accordance with the requirements set forth in the IEEE PSPB Operations Manual (sections 8.2.1.C & 8.2.2.A). Each published article was reviewed by a minimum of two independent reviewers using a single-blind peer review process, where the identities of the reviewers are not known to the authors, but the reviewers know the identities of the authors. Articles will be screened for plagiarism before acceptance.

Corresponding authors from low-income countries are eligible for waived or reduced open access APCs.

Special Issue on Revolutionizing Healthcare Informatics with Generative AI: Innovations and Implications
Submission Date: 2024-10-30

Generative AI has the potential to revolutionize healthcare informatics across multiple areas, such as data synthesis,
image enhancement, disease prediction and diagnosis, drug discovery, medical documentation, and personalized
healthcare. It offers opportunities to overcome data scarcity and privacy concerns through synthetic data generation and
supports accurate disease interpretation and diagnosis through image quality enhancement. Generative AI models also
enable disease likelihood prediction, early condition detection, precise diagnoses, and personalized treatment plans for
improved patient care. Additionally, it facilitates drug development through simulated interactions and automates
medical documentation to reduce administrative burdens. While the potential impacts are promising, ethical
considerations, patient privacy, and regulatory approval for AI-centric approaches must be carefully addressed. This
special issue seeks to publish novel research in generative AI for healthcare, highlighting advancements in healthcare
informatics.

This Special Issue will focus on research targeting the development of Generative AI systems for Healthcare Informatics.

We invite original research contributions for publication, focusing on the following topics in healthcare informatics (and
is/are not limited to):
• Generative AI for medical image analysis
• Natural language processing (NLP) and generative AI in healthcare informatics
• Generative AI for personalized medicine and drug discovery
• Ethical considerations and challenges in generative AI for healthcare informatics
• Real-world implementations with case studies
• Privacy and security challenges
• User experience, human-computer interaction, and design considerations
• Generative AI for public health initiatives, population health management, and healthcare interventions
• Ethical considerations, legal frameworks, and policy Generative AI in Health Informatics

Guest Editors
Ashutosh Kumar Singh, Indian Institute of Information Technology Bhopal, India (ashutoshkumarsingh@ieee.org)
Jitendra Kumar, National Institute of Technology Tiruchirappalli, India (jitendra@nitt.edu)
Deepika Saxena, University of Aizu, Japan (deepkia@u-aizu.ac.jp)
KC Santosh, University of South Dakota, United States of America (santosh.kc@usd.edu)

Key Dates
Deadline for Submission: 30 Oct, 2024
First Reviews Due: 15 Nov, 2024
Revised Manuscript Due: 10 Dec, 2024
Final Decision: 10 Jan, 2025

Special Issue on Internet of Medical Things (IoMT) Based Healthcare Informatics System, Emerging Techniques, Challenges, and Future Directions
Submission Date: 2024-10-31

The Internet of Medical Things (IoMT) is an organization of wireless, interconnected, and smart medical smart devices.
Which can collect and communicate sensitive information over the network without the intervention of human and
computer system interaction. The IoMT promises several advantages to modernize and enhance the healthcare
conveyance to proactively anticipate health issues and treat, diagnose, and observe patients in real-time in and outside
the hospital surrounding. Globally, many governments and high decision-makers are executing new strategies to convey
healthcare services using technology. Its aim is to respond to new healthcare issues such as the novel COVID-19
pandemic. It is now becoming essential to understand how conventional and upcoming IoT technologies may assist
health systems in providing safe and effective care for human beings.

To offer reliable and effective healthcare services with various medical IoT policies is one of the key challenges faced
in healthcare applications. Industries still repetitively utilize their trademarked protocols for tackling these smart devices.
This can cause issues, particularly when collecting huge amounts of data from medical servers.

Moreover, connectivity concerns persist, as data collecting by smart devices such as cell phones can be disrupted by a
variety of elements in the environment. To address these issues, the IoMT-based healthcare system has excessive
capabilities to enhance the reliability and effectiveness of the modern healthcare system.

This Special Issue aims to highlight the most recent innovative work in the IoMT and WBANs system, which have the
potential to provide reliable, secure, inconspicuous, and continuous QoS healthcare monitoring and well-
organized healthcare services.

The topics of this Special Issue include, but are not limited to the following:
 Recent trends in IoMT and WBANs in healthcare informatics system.
 Contemporary IoMT healthcare informatics monitoring models.
 Issues in data communication, incorporation, and analysis in IoMT Healthcare informatics system.
 Modern AI based WBANs healthcare monitoring system .
 The privacy, reliability, and security solutions regarding sensitive healthcare informatics system.
 Fault-Tolerance in IoMT in healthcare informatics system.
 Robust Network design and connectivity issues in IoMT based healthcare informatics system.
 Fog computing, Edge computing and Fuzzy logic-based solutions in the healthcare informatics system
 The integration of IoMT devices with other emerging technologies.
 IoMT and WBANs integration for healthcare and other emerging applications.
 IoMT and WBANs and current Covid-19 pandemic healthcare informatics system.

Guest Editors
Ali Kashif Bashir, Manchester Metropolitan University, UK, a.bashir@mmu.ac.uk
Chrysostomos Chrysostomou, Frederick University, Cyprus, ch.chrysostomou@frederick.ac.cy
Gulzar Mehmood, IQRA National University, Pakistan, gulzar.mahmood@uom.edu.pk

Key Dates
Deadline for Submission: 31 Oct, 2024
First Reviews Due: 05 January,2025
Revised Manuscript Due: 01 Feb, 2025
Final Decision: 01 March, 2025

Special Issue on Swarm Intelligence in Healthcare Data Analysis for Early Cancer Detection
Submission Date: 2024-10-31

Swarm Intelligence (SI) is a form of collective learning and decision-making based on decentralized, self-organized
systems. Utilizing SI healthcare tackles the propagation of attacks inside interconnected healthcare organizations and
ensures the completeness of the healthcare ecosystem based on security and resilience. In the healthcare sector, swarm
intelligence is being utilized to improve diagnosis and treatment, leading to better patient outcomes and more efficient
healthcare systems. SI algorithms can be integrated into the healthcare environment for disease diagnosis and treatment
of diseases including cancer, heart diseases, tumours, and cardiology, it has been applied in the areas of disease diagnosis
and treatment. It has been used to predict cancer at the early stage and solve the complex problem. In addition, it can
quickly learn how cancer cells become resistant to anticancer drugs, which can help improve drug development and
adjust drug use. Normally, SI algorithms are used in PSO, ICA, FA and IWO for the diagnosis of cancer in solving the
optimization of the problem. This will in turn enhance the overall effectiveness of SI in data analysis. However, there
are several challenges associated with applying swarm intelligence to cancer-related problems. Some of these challenges
include the complexity of cancer, analysis of cancer, validation and clinical translation, resistance and adaptation, etc.
These challenges have to be overcome by improved algorithms and models making them more efficient, scalable, and
better suited for handling large-scale and high-dimensional cancer datasets. Alternatively, the main application of SI in
cancer detection is image analysis and pattern recognition which helps to identify patterns and features associated with
cancerous tissues, aiding in early detection and accurate diagnosis.

In the field of SI in cancer research several future advancements are anticipated. Some potential future advancements in
SI are being developed in the area of cancer research integration with multi-omics data, swarm robotics for targeted drug
delivery etc. In this special issue entitled “Swarm Intelligence in Healthcare Data Analysis for Early Cancer Detection
aims to explore various aspects including adaptability, dimensionality, detection and prevention, decision-making, future
advancements and other areas of healthcare data with swarm intelligence technology.

Topics of interest include, but are not limited to, the following:
• Recognition of patterns for accurate decision-making of cancer related problems using swarm intelligence
• Recent development in cancer detection with high dimension healthcare data based on Swarm intelligence
• Role of decentralized Swarm intelligence environment in analyzing and predicting the huge volume of cancer data
• Analysis of anticancer drugs in patients using SI
• Future advancement of curing cancer using PSO algorithm in swarm intelligence
• Exploring various algorithms used for cancer prediction in swarm intelligence
• Clinical integration challenges in cancer detection using SI: possibility and opportunities
• Challenges in the application of swarm intelligence algorithm in cancer diagnosis
• Swarm robotics for target drug delivery in cancer therapy
• Future perspective of real-time monitoring of cancer using swarm intelligence

Guest Editors
Dr. Aleksandra Kawala-Sterniuk, Opole University of Technology, Poland. biomed.bspl@gmail.com ,
a.kawala-sterniuk@po.edu.pl
Dr. Adam Sudol - University of Opole, Poland. dasiek@uni.opole.pl
Dr. Mariusz Pelc - University of Greenwich, London.. radek.martinek@vsb.cz
Dr. Radek Martinek - VSB-Technical University of Ostrava, Czech Republic. radek.martinek@vsb.cz

Key Dates
Deadline for Submission: 31 Oct, 2024
First Reviews Due: 05 Jan, 2025
Revised Manuscript Due: 25, Feb 2025
Final Decision: 15 Apr, 2025

Special Issue on Privacy-Preserving Cloud Computing with Federated Learning for Healthcare Data
Submission Date: 2024-11-01

The early identification of illnesses is becoming more accessible and reasonably priced because of the smart healthcare system's quick development. Nonetheless, the primary cause of anxiety is the computer's possible major confidentiality risk. Due to the scattered nature of healthcare data as well as its rapid expansion, industrial cloud computing and the growing Internet of Things have completely changed the healthcare sector. The perfect guarantee of the safety of healthcare data cannot be achieved with the current privacy-preserving measures. Rather than being subject to a beyond assault, the majority of healthcare data stored on cloud servers is under attack from within. Among the most important issues facing the healthcare sector are the security and privacy of patient data.

Convolutional neural networks were used to classify normal and deviant users from the analysed information. The aberrant users were subsequently evaluated and eliminated from the repository, in addition to the availability of the health information, according to the integration of blockchain technology with a federated learning mechanism that employs secrecy.

Federated learning serves as a unique artificial intelligence method that protects privacy while providing contextual information about data in smart cities. In order to preserve privacy in intelligent healthcare, a secure architecture supported by federated learning and digital currency technology is needed. Cryptocurrency-based cloud services for IoT are utilised to ensure confidentiality and safety. Affordable gadget-acquiring applications, such as those in healthcare, use supervised learning systems. One of the main issues with healthcare data is security and privacy. Safeguarding private information is a common definition of privacy. The growing volume of medical data that is becoming a crucial component of patient treatment is the cause of the assault on medical information.

Exterior antagonists are usually prevented by identification and encryption mechanisms, but inner hostile activity is the main problem since it leads to assaults like recollect and interruption of service attacks, among others. Furthermore, as medical data involves important data regarding patients, secrecy is a critical concept in the therapeutic system. FL is a decentralised method that gives several network nodes individualised, resilient, and privacy-aware data. It offers enhanced privacy protection and efficiency in a variety of software, making it very promising. This special issue addresses the latest developments in federated learning to tackle this issue within the framework of computation that protects privacy. Federated learning offers significant advantages to healthcare systems, excellent protection and privacy assurances, and the ability to develop and employ global AI models across numerous decentralised data sources. The origins, goals, explanations, platforms, and potential uses of federated learning are presented in this special issue as an alternative model for creating trustworthy, privacy-preserving AI societies.

Topics of interest include, but are not limited to, the following:
• A smart medical system powered by federated learning and protects privacy
• Cloud computing-based federated learning for privacy-preserving Web of medical things
• Federated learning as an architecture for Smart healthcare dataset privacy preservation
• Regarding healthcare data networks, a privacy-preserving federated learning architecture
• IoT-enabled medical facilities using homomorphic safeguarding for privacy-preserving federated learning
• Collaborative learning and a reliable, privacy-preserving modular ensembles in healthcare
• A structure protects privacy for federated learning in intelligent health systems
• A federated learning-based privacy-preserving cloud computing platform for managing health
• Federated learning-based cloud computing with privacy preservation for intelligent utilities
• Implementing Federated Machine Learning to Leverage Privacy Preservation for Health Care Facilities
• A secure and private federated learning application platform for Internet of Things

Guest Editors
Dr. Adnan Shahid Khan - Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Malaysia, adnanskhan084@gmail.com
Dr. Irshad Ahmed Abbasi - University of Bisha, Bisha, Saudi Arabia, aabasy@ub.edu.sa
Dr. Kashif Nisar - Swinburne University of Technology, Sydney, New South Wales, Australia, knisar@swin.edu.au

Key Dates
Deadline for Submission: 01 Nov, 2024
First Reviews Due: 01 Jan, 2025
Revised Manuscript Due: 01 Mar, 2025
Final Decision: 01 May, 2025

Special Issue on Impact of Machine Learning on Personalized Medicine in Public Health
Submission Date: 2024-11-20

The integration of advanced analytics and healthcare has opened new horizons in adapting medical measures to
individual patients revolutionizing the field of personalized medicine. Normally, machine learning algorithms have the
capacity to analyse vast amounts of diverse patient data. These data may include genetic information, clinical records,
lifestyle factors and other essential data. Using the algorithms healthcare professionals can extract invaluable insights
and patterns from the data providing customized solutions based on individual characteristics and needs. Machine
learning also plays an important role in precision diagnostics and treatment recommendations. It has the capabilities to
analyse patient characteristics, genetic factors, and treatment responses and predict drug reactions, adverse events and
optimal dosage levels. This enables healthcare providers to suggest medicines with minimized side effects by
improving more therapeutic outcomes. Moreover, the application of machine learning in public health will provide
more accurate prediction in infectious disease surveillance and outbreak. Further, machine learning algorithms can also
detect early warning signs of disease outbreaks by analysing multiple data sources such as social media feeds, climate
data and geographical information. This will enable public health officials to carry out timely interventions thereby
mitigating the impact of infectious diseases on population health. From disease prediction to outbreak surveillance,
machine learning has the capacity to reshape the landscape of public health, enabling evidence-based decision-making
and ultimately improving health outcomes for individuals and communities. This special issue aims to attract original
research papers from professionals in this domain area to provide insights that can shape the future of personalized
medicine and improve public health outcomes.

Potential topics include, but are not limited to:
• Machine learning approaches for optimizing precision medicine treatment plans
• Genomic data analysis and machine learning for drug response prediction
• Machine learning in identifying genetic markers for targeted therapies
• Applications of machine learning in predicting treatment outcomes and adverse reactions
• Enhancing interpretability and transparency of machine learning models in healthcare
• Overcoming bias and improving fairness in machine learning-based personalized medicine
• Ethical considerations and privacy protections in the era of machine learning and personalized medicine
• Data sharing and governance frameworks for effective collaboration in public health
• Machine learning for early detection and intervention in infectious disease outbreaks
• Automation of public health surveillance systems using machine learning
• Machine learning-enabled decision support systems for healthcare providers
• Real-time monitoring and prediction of public health indicators using machine learning
• Machine learning for optimizing clinical trial design and patient selection in drug development.

Guest Editors
Shadi Mahmoud FalehAlZu’bi, Al-Zaytoonah University of Jordan, dr.shadi.alzubi@gmail.com
MaysamAbbod, Brunel University London, Uxbridge, maysam.abbod@brunel.ac.uk
Ashraf Darwish, Helwan University, Cairo, ashraf.darwish.eg@ieee.org)

Key Dates
Deadline for Submission: 20 Nov, 2024
First Reviews Due: 25 Jan, 2025
Revised Manuscript Due: 30 Mar, 2025
Final Decision: 20 May, 2025

Special Issue on Fusion of Artificial Intelligence and Metaverse Technologies for Personalized and Predictive Healthcare Capabilities
Submission Date: 2024-11-30

The faster maturity and stability of prominent metaverse technologies (AR, VR, Web 3.0, Blockchain, 5G Advanced,
Edge Computing, etc.) associated with pioneering AI algorithms have laid a stimulating foundation for realizing digitally
transformed industry houses. Healthcare is being touted as the forefront of getting thoroughly disrupted and transformed
through this strategically sound fusion of metaverse and AI.

This unique combination has the innate strength to open up unprecedented opportunities for envisaging and realizing
advanced healthcare facilities. Medical electronics, instruments, scanners, and equipment data gets collected, cleansed,
and crunched locally by embedded and efficient AI models. This on-device data processing emits actionable insights in
time. Thereby smart hospital environments can be set up and sustained. Real-time intelligent healthcare services can be
dynamically developed and delivered to patients, doctors, surgeons, medical scientists, and experts. Healthcare providers
can access all kinds of medical and patient data with the metaverse. Metaverse technologies and tools elegantly fulfill
sharing, personalization, and collaboration requirements. Medical education is to see a drastic shift with the blending of
metaverse and AI.

This convergence promotes language learning, coding/programming education, teaching support, student engagement,
higher-order thinking, and collaborative learning. Integrating these state-of-the-art technologies can improve medical
literacy, personalized patient care, and better health outcomes. This special issue seeks to bring together research that
explores the various facets of this fusion and its implications for healthcare, medical education, and beyond.

Topics of interest include, but are not limited to, the following:
• Use of AR, VR, and AI in delivering tailored medical education.
• Adaptation of learning content to address individual learner needs and preferences.
• Personalized feedback and assessment using AI technologies.
• Integration of metaverse and extended reality technologies in language learning for healthcare professionals.
• Computational thinking and its role in healthcare.
• AI-assisted teaching support, such as answering frequently asked questions and generating questions.
• Evaluating students' writing and performance using AI technologies.
• Enhancing student motivation and engagement through human-like interactions and natural language output.
• Developing analytical, critical thinking, and reflection skills using metaverse and extended reality technologies.
• AI-driven tools for problem-solving and decision-making in healthcare.
• AI models for biomedical image processing in healthcare
• Privacy and security in healthcare
• Fostering ethical considerations and responsible use of AI in medical education.
• Supporting group discussion and interaction using AR, VR, and AI.
• Role of the metaverse in facilitating collaborative learning experiences.
• Challenges and opportunities in collaborative learning processes in healthcare and medical education.

Guest Editors
Khursheed Aurangzeb, King Saud University, kaurangzeb@ksu.edu.sa
Shuihua Wang, Xi'an Jiaotong-Liverpool University; shuihuawang@ieee.org
Yudong Zhang, Southeast University, yudongzhang@ieee.org
Pushan Kumar Dutta, Amity University Kolkata, India pkdutta@kol.amity.edu
Bharat Bhushan, Sharda University, India, bharat_bhushan1989@yahoo.com

Key Dates
Deadline for Submission: 30 Nov, 2024
First Reviews Due: 05 Feb, 2025
Revised Manuscript Due: 01 April, 2025
Final Decision: 01 June, 2025

Special Issue on Securing Tomorrow’s Care: Navigating Privacy and Security Challenges in the Integration of AI Algorithms for Healthcare and Biomedical Applications
Submission Date: 2024-12-01

Artificial intelligence (AI) is on the verge of reshaping the healthcare and biomedical sectors, presenting unparalleled
prospects for improved patient care. As we embark on this transformational path, incorporating AI algorithms into
healthcare systems presents intricate issues in terms of privacy and security. This special issue is dedicated to exploring
the dynamic convergence of AI and healthcare, with the goal of understanding the complexities involved in ensuring
future healthcare. In the rapidly growing field of AI-driven healthcare, the interdependence of technical advancement
and patient welfare necessitates a thorough analysis of security dynamics. This special issue seeks contributions from
scholars, researchers, and practitioners to further the discussion on privacy and security difficulties. Topics include
protecting sensitive health data, resolving ethical concerns, and researching the secure integration of augmented reality.
Through this collaborative effort, our goal is to fully understand the complexities of this assimilation while establishing
a foundation for promoting the standards of security and privacy in the era of AI-enhanced healthcare and biological
applications.

This special issue focuses specifically on AI's privacy and security aspects in healthcare. This SI aims to provide a
comprehensive understanding of the challenges and opportunities presented by AI in healthcare, ultimately contributing
to the advancement of patient-centered, secure, and ethical AI-driven healthcare solutions. We encourage original
research, reviews, and case studies examining the security problems emerging from integrating AI algorithms into
healthcare and biomedical applications. Submissions may include, but are not limited to:

• Privacy-preserving strategies in AI-enhanced healthcare interactions.
• Security implications for AI-driven medical training simulations.
• Securing patient data in the context of AI-integrated biomedical applications.
• Ethical dimensions of employing AI algorithms in healthcare environments.
• Augmented reality applications and their security implications in healthcare.
• Integration of healthcare the Internet of Things (IoT) with AI algorithms.
• Security challenges in AI-driven clinical environments.
• Ensuring informed consent and fair access to AI-enhanced healthcare services.

Guest Editors
Khalid Mahmood, National Yunlin University of Science and Technology, khalid@yuntech.edu.tw
Ali Kashif Bashir, Manchester Metropolitan University, Dr.alikashif.b@ieee.org
Hadis Karimipour, University of Calgary, hadis.karimipour@ucalgary.ca
Wei Wang, Beijing Institute of Technology, and Shenzhen MSU-BIT University, (ehomewang@ieee.org)

Key Dates
Deadline for Submission: 01 Dec, 2024
First Reviews Due: 15 Feb, 2025
Revised Manuscript Due: 01 Apr, 2025
Final Decision: 01 May, 2025

Special Issue on Camera-based Health Monitoring in Hospitals
Submission Date: 2024-12-01

Remote cameras have been used to measure physiological signals from human face and body, thereby eliminating
mechanical contact with the skin like 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, perfusion index and physiological markers/indicators that have diagnostic capabilities. Image and video
analysis also permit the measurement of human semantics 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 contact-based biomedical sensors. Camera-based health monitoring will bring a rich set of compelling healthcare applications that directly improve upon contact-based monitoring approaches in hospital care units (e.g.
ICU, NICU), improving patients’ care experience and quality of treatment. After years of R&D in this field, it is time to
bring the concepts and prototypes (including setups and algorithms) one step further to the hospitals to demonstrate
their actual performance and values via clinical trials. This is an important step towards the transformation of camera-
based monitoring technology into a medical device. This special issue focuses on the latest developments and clinical
showcases pertaining to Camera-based Health Monitoring in Hospitals, specifically on innovation, validation and
demonstration in clinical environments.

Only high-quality and original contributions will be considered. Topics of interest include, but are not limited to
• Novel/improved camera sensors and sensor fusion for patient care and monitoring, and image/signal processing algorithms that create new measurements (e.g. physiological signals or contextual signals) for health informatics.
• Novel developments and applications of camera-based health monitoring in high-acute and low-acute clinical settings.
• Novel integration between cameras and existing medical devices like for MR/CT triggering and gating.
• Novel camera-based applications with medical purpose, including automotive, fitness, assisted-living homes, mobile healthcare, etc.
• Clinical trials of camera-based health monitoring in hospital care units, including Intensive Care Unit (ICU), High Dependency Unit (HDU), Neonatal Intensive Care Unit (NICU), Coronary Care Unit (CCU), Thoracic Surgery Department, Pneumology Department, Emergency Department, etc.
• Clinical trials of camera-based health monitoring in sleep centres, rehabilitation centres, confinement centres, senior centres, etc.
• Clinical trials of camera-based health monitoring in telemedicine that connect in-home monitoring with hospital services (e.g. chronic disease management).
• New benchmarks, datasets and literature reviews for camera-based healthcare applications in hospitals.

Special Issue on Advancing Medical Image Analysis through Self-Supervised Learning: Innovations, Applications, and Future Directions
Submission Date: 2024-12-15

In modern medical diagnostics, medical image analysis, particularly the interpretation of medical imaging data such as
MRI, CT scans, X-rays, Ultrasound, and PET scans, is a primary area of importance. Medical image analysis is mainly
concerned with processing and analyzing medical images to extract useful information that helps in making precise
diagnoses. However, a significant challenge in advancing this field is the availability of labelled data. To address this
critical concern and to advance the area of medical image analysis, this special issue focuses on exploring Self-
Supervised Learning (SSL), which is extremely helpful in cases where the available data is unlabeled and holds the
potential to transform the field of medical image analysis. Traditional SSL techniques cannot be directly applied to
medical images as they are different from ordinary images. There are certain significant concerns related to the
applicability and effectiveness of SSL for medical data that need the attention of researchers. Moreover, it is extremely
important to explore which type of SSL technique works best from predictive, generative, and contrastive learning
perspectives.

This special issue, in this regard, invites cutting-edge research and novel contributions in the field of SSL for medical
image analysis. In addition to original research articles, this SI welcomes systematic literature reviews (SLRs) and critical
evaluation studies that review existing SSL techniques and evaluate them for their applicability in practical medical
settings. The evaluation studies should address the problems of using SSL in healthcare, such as trust and privacy of user
data, the utilization of explainable AI to assist medical practitioners in making quick and accurate diagnoses.

Topics of interest include, but are not limited to, the following:
• Novel algorithms and architectures for SSL in medical imaging.
• SSL applications in disease diagnosis, patient monitoring, medical response, patient care management, etc.
• Integration of SSL with clinical metadata, i.e., methodologies to incorporate clinical metadata into SSL frameworks.
• The SSL contrastive pre-training methods in medical imaging, such as Multi-Instance Contrastive Learning (MICLe)
for enhancing model robustness and accuracy in medical diagnostics.
• 3D SSL applications, e.g., investigating the extension of self-supervised algorithms to 3D medical imaging.
• Knowledge-guided self-supervised Vision Transformers for improving interpretability and performance on tasks like
lung and heart segmentation and disease classification from chest X-rays, etc.
• Cross-modal learning in medical image analysis
• Ethical considerations and bias mitigation in self-supervised learning.

Guest Editors
Jawad Ahmad, Edinburgh Napier University, UK, j.ahmad@napier.ac.uk
Qammer Abbasi, University of Glasgow, UK, Qammer.Abbasi@glasgow.ac.uk
Syed Aziz Shah, Coventry University, UK, syed.shah@coventry.ac.uk
Nikolaos Pitropakis, Edinburgh Napier University, UK, n.pitropakis@napier.ac.uk
Wadii Boulila, Prince Sultan University, Kingdom of Saudi Arabia, wboulila@psu.edu.sa

Key Dates
Deadline for Submission: 15 Dec, 2024
First Reviews Due: 15 Feb, 2025
Revised Manuscript Due: 15 Apr, 2025
Final Decision: 15 June, 2025

Special Issue on Application of computational techniques in drug discovery and disease treatment Part II
Submission Date: 2024-12-31

Computational techniques have been successfully applied in the field of drug discovery and disease treatment. Specially,
computer-aided drug design, computational drug repositioning, drug-target interactions prediction and synergistic drug
combinations prediction based on heterogeneous biological data have become critical topics in the search of drugs and
therapeutic targets for various diseases. The study of these topics is not only to provide better understandings of the
mechanisms of disease progression and drug therapy, but is also critical to the development of new drugs and the
improvement of treatments. As is well-known, the processes for drug discovery and development are still time
consuming, expensive and limited to small-scale research even nowadays. With the development of new experimental
techniques, vast amounts of datasets now flow through the different stages of drug development and disease treatment,
and there is a major requirement to extract knowledge from these datasets and employ them to improve these processes
in all respects. Therefore, there is a strong incentive to develop powerful computational methods capable of mining these
datasets efficiently in order to provide new predictions for experimental scientists and narrow the scope of candidates to
accelerate drug discovery. For the potential prediction results with higher scores, biological experiments could be
implemented for validation. Recently, the applicability of computational techniques has been extended and broadly
applied to nearly every stage in the drug discovery and development workflow. This special issue is the continuation of
our previous special issue (Application of computational techniques in drug discovery and disease treatment) in IEEE
JBHI.

The proposed special issue will focus on novel computational techniques in drug discovery and disease treatment. We
will invite investigators to contribute research article and reviews of describing recent findings which use computational
techniques for the research of drug discovery and disease treatment.

Potential topics include, but are not limited to
• Drug–target interaction prediction
• Drug-drug interaction prediction
• Synergistic drug combination prediction
• Computer-aided drug design
• Computational drug repositioning
• Drug effect and side-effect prediction
• Adverse drug reactions prediction
• Anti-cancer drug response prediction
• Microbe-drug association prediction
• Small molecule drug-ncRNA interaction prediction
• Drug network analysis

Guest Editors
Prof. Xing Chen, Jiangnan University, xingchen@amss.ac.cn
Prof. Chun-Chun Wang, Jiangnan University, chunchunwang@jiangnan.edu.cn

Key Dates
Deadline for Submission: 31 Dec, 2024
First Reviews Due: 28 Feb, 2025
Revised Manuscript Due: 31 May, 2025
Final Decision: 31 Jun, 2025

Special Issue on Contactless Sensing and Intelligent Processing for Health Monitoring and Early Disease Detection
Submission Date: 2024-12-31

Health monitoring and early disease detection hold immense significance in contemporary healthcare, offering a
paradigm shift from reactive to proactive and preventive approaches. Detecting health issues in their nascent stages
allows for timely intervention, often before symptoms manifest. However, traditional disease detection methods often
involve invasive procedures or require individuals to be tethered to monitoring devices. Contactless sensing technologies,
such as infrared sensors, radar, and computer vision, enable the non-intrusive collection of vital health data. For instance,
wearable devices equipped with these sensors can monitor parameters like heart rate, respiratory rate, and body
temperature in real-time, providing a continuous stream of data without disrupting the user's daily activities. At the same
time, intelligent processing, fueled by artificial intelligence (AI) and machine learning (ML) algorithms, plays a pivotal
role in making sense of the vast amounts of data generated by contactless sensing devices. Combining contactless sensing with intelligent processing technologies can greatly benefit future healthcare. In light of this potential, this special section provides a venue to cover comprehensively algorithms, frameworks, technologies, and applications of contactless
sensing and intelligent processing for health monitoring and early disease detection.

This special issue focuses on the crossroads among scientists, industry practitioners, and researchers from the domains
in smart healthcare, wireless communication, internet of things, artificial intelligence, big data, smart sensing, etc. This
special issue will cover comprehensively algorithms, frameworks, and technologies for advanced contactless sensing
and intelligent processing technologies. Technical scope of the proposal includes, but not limited to:

 Contactless sensing for remote health monitoring
 Sensor fusion for comprehensive health monitoring
 Intelligent communications for health sensor data
 Intelligent communication and processing for disease detection
 Explainable AI for health monitoring and disease detection
 Real-time monitoring and feedback systems for heath monitoring
 Machine/Deep learning algorithms for anomaly detection for disease detection
 Federated learning approaches for health monitoring and disease detection
 Large language models and its applications in health monitoring and disease detection
 User-friendly and culturally sensitive contactless health monitoring devices
 IoT system architectures in health monitoring and early disease detection
 Security, trust and privacy computing for health monitoring and disease detection
 Real-world applications of health monitoring and disease detection

Guest Editors
Kai Fang (Leading GE), Zhejiang A&F University, kaifang@zafu.edu.cn
Hadis Karimipour, University of Calgary, hadis.karimipour@ucalgary.ca
Thippa Reddy Gadekallu, Vellore Institute of Technology, thippareddy@ieee.org
Tingting Wang, Macau University of Science and Technology, tingtingwang@ieee.org
Syed Hassan Ahmed, California State University, sh.ahmed@ieee.org

Key Dates
Deadline for Submission: 31 Dec, 2024
First Reviews Due: 05 Feb, 2025
Revised Manuscript Due: 01 Mar, 2025
Final Decision: 01 May, 2025

Special Issue on The cutting-edge artificial intelligence techniques and their applications in drug discovery
Submission Date: 2024-12-31

The realm of computer science has been revolutionizing numerous industries with its rapid advancements, and the field
of drug discovery is no exception. Recent breakthroughs in various sub-disciplines have opened new frontiers, offering
unprecedented opportunities to enhance and expedite the drug discovery process. This special issue focuses on how
cutting-edge techniques in computer science, particularly large language models (LLMs), prompt learning, generative
models, multi-modal representation learning, pre-training models, graph neural networks, and geometry deep learning,
can be leveraged to revolutionize the landscape of drug discovery.

This Special Issue will focus on cutting-edge artificial intelligence techniques and their applications the drug discovery.
The Special Issue will be comprised of research articles and reviews submitted by invited investigators, describing recent
findings that use cutting-edge artificial intelligence techniques for the research of drug discovery.

Topics of interest include, but are not limited to, the following:
• Large Language Models (LLMs) for drug discovery
• Prompt Learning for Drug Discovery
• Generative Models for drug discovery
• Multi-Modal Representation Learning for drug discovery
• Pre-Training Models for Drug Discovery
• Graph Neural Networks for drug discovery
• Geometry Deep Learning for Drug Discovery

Guest Editors
Wen Zhang, Huazhong Agricultural University, zhangwen@mail.hzau.edu.cn
Qi Zhao, University of Science and Technology Liaoning, zhaoqi@lnu.edu.cn

Key Dates
Deadline for Submission: 31 Dec, 2024
First Reviews Due: 05 Feb, 2025
Revised Manuscript Due: 01 Mar, 2025
Final Decision: 01 Apr, 2025

Special Issue on AI-Generated Content-Based Healthcare – Transforming the Landscape of Patient Care and Medical Services
Submission Date: 2024-12-31

Artificial intelligence and content-based healthcare are evolving rapidly, presenting substantial opportunities for
researchers, developers, and industry experts to explore innovative concepts and redefine the limits of what is possible
in the healthcare sector.

One of the most fascinating aspects of this convergence is the potential for AI-generated content to improve patient
care and streamline medical services. As AI-driven systems advance and become more sophisticated, they can provide
novel ways to assist healthcare professionals in diagnostics, treatment planning, and patient monitoring. For example,
AI-powered diagnosis and treatment recommendations, automated medical imaging analysis, and personalized
medicine through AI-generated content can all contribute to better healthcare outcomes and more efficient practices.
Another crucial component of this integration is the potential for innovative methods of communication and patient
engagement within the healthcare system. Through AI-generated content, patients can receive personalized health
information and educational materials, opening new possibilities for self-care, adherence to treatment plans, and overall
well-being. Natural language processing can also facilitate more effective communication between healthcare
professionals and patients, leading to better patient experiences.

However, these new opportunities come with challenges and risks. Privacy and security are critical factors to consider
when implementing AI-generated content in healthcare, as patients' sensitive information and medical data could be
vulnerable. Additionally, ethical concerns must be addressed when developing and deploying AI-driven systems that
could significantly impact patient care and medical professionals' decision-making processes.

To examine these opportunities and challenges, it is essential to develop responsible solutions that enhance patient care
and healthcare efficiency while ensuring privacy, security, and ethical considerations. This special issue will focus on
the intersection of AI-generated content and healthcare, with an emphasis on how these innovative systems can
transform the landscape of patient care and medical services.

We invite authors to submit original research articles, review articles, case studies, and innovative applications related
to AI-generated content-based healthcare.

Focus (topics/focus areas)

The list of possible topics includes, but is not limited to:
1. AI-powered diagnosis and treatment recommendations
2. Automated medical imaging analysis and interpretation
3. Personalized medicine through AI-generated content
4. Natural language processing for healthcare communication
5. AI-driven health monitoring and prediction
6. Data-driven health education and patient engagement
7. Ethical considerations, privacy, and security in AI-generated healthcare content
8. Integration of AI-generated content in electronic health records
9. AI-assisted decision-making in healthcare management
10. AI-generated content for mental health and well-being

Guest Editors
• Weizheng Wang, City University of Hong Kong, Hong Kong SAR, China, weizhwang7-c@my.cityu.edu.hk
• Huakun Huang, Guangzhou University, Guangzhou, China, huanghuakun@gzhu.edu.cn
• Kapal Dev, Munster Technological University, Ireland, kapal.dev@ieee.org
• Thippa Reddy Gadekallu, Zhongda Group, China, thippa@zhongda.cn

Key Dates
Deadline for Submission: 31 Dec, 2024
First Reviews Due: 05 Feb, 2025
Revised Manuscript Due: 01 Mar, 2025
Final Decision: 01 May, 2025

Special Issue on Orchestrating Biomedical Breakthroughs through the Fusion of NLP Techniques and GPT Transformers
Submission Date: 2024-12-31

The integration of natural language processing (NLP) techniques with Generative Pre-trained Transformer (GPT)
models shows great promise for biomedical applications. GPT models possess impressive language generation abilities
and contextual understanding, which can be combined with NLP to enhance various biomedical tasks. Biomedical text
generation, medical question-answering systems, and clinical decision support systems can all benefit from this
integration. Some potential applications include generating scientific abstracts, literature summaries, and patient
reports. Medical question-answering systems can provide accurate responses to queries about medical literature,
treatments, and diseases. Clinical decision support systems can aid healthcare professionals in diagnosing diseases,
suggesting treatments, and predicting patient outcomes. Moreover, the integration can be applied to drug discovery,
biomedical information extraction, and clinical documentation improvement. However, there are several challenges in
integrating NLP techniques with GPT models for biomedical applications. Limited availability of high-quality labelled
data specific to the biomedical domain makes it difficult to effectively fine-tune GPT models. Obtaining large-scale,
annotated biomedical datasets is essential to train models that understand the complex terminology and context of the
medical field. Furthermore, the rapid evolution of medical knowledge and terminology requires continuous updates
and retraining of models to ensure their relevance and accuracy. Interpreting GPT models is another challenge, as
understanding their decision-making process and ensuring transparency is crucial for building trust and facilitating
adoption in biomedical applications. Ethical considerations regarding patient privacy, data security, and bias mitigation
are also vital when dealing with sensitive biomedical information. Addressing these challenges is essential to fully
harness the potential of NLP and GPT integration for biomedical applications and ensure their safe and effective
deployment in real-world healthcare settings. This special issue focuses on the latest developments and technologies
pertaining to Biomedical Breakthroughs through the Fusion of NLP Techniques and GPT Transformers, specifically
on innovation, validation and demonstration in biomedical applications.

Only high-quality and original contributions will be considered. Topics of interest include, but are not limited to
• Augmenting clinical decision-making with NLP and GPT
• Biomedical text generation with GPT models
• Enhancing medical question-answering systems using NLP techniques
• GPT-based drug discovery and development
• NLP and GPT for improving clinical documentation accuracy
• Biomedical named entity recognition using GPT models
• Adverse drug event detection through NLP and GPT
• GPT-driven analysis of electronic health records for clinical insights
• Context-aware biomedical information extraction using NLP and GPT
• Language generation for patient education using NLP and GPT
• GPT-powered natural language understanding in healthcare chatbots
• Sentiment analysis of patient feedback using NLP and GPT
• GPT-based analysis of social media data for public health surveillance
• Personalized healthcare recommendations using NLP and GPT

GuestEditors
Rutvij H. Jhaveri, Pandit Deendayal Energy University, India (rutvij.jhaveri@sot.pdpu.ac.in)
Huaming Wu, Tianjin University, China (whming@tju.edu.cn)
Haoran Chi, Universidade de Aveiro, Portugal (haoran.chi@ua.pt)
Dhavalkumar Thakker, University of Hull, UK (D.Thakker@hull.ac.uk)

KeyDates
Deadline for Submission: 31 st December, 2024
First Reviews Due: 1 st March, 2025
Revised Manuscript Due: 1 st April, 2025
Final Decision: 31 st May, 2025

Special Issue on Swarm Intelligence for Security and Privacy Services in Digital Healthcare Systems
Submission Date: 2025-01-01

In digital healthcare systems, security and privacy are of vital importance. As the healthcare industry undergoes the
digital transformation, more data is being stored electronically, making data security and privacy protection essential.
First, security mainly involves the protection of medical data, which is highly sensitive, including patients’ personal
information, diagnosis results and treatment plans. To ensure the data security, a range of security measures need to be
implemented, such as data encryption, access control, and secure auditing. Second, the privacy preservation is also a
crucial consideration, involving the rights and interests of patients.

Yet, cost always come with benefits. Although more modern information and communication technologies are merged
into digital healthcare systems, it becomes increasingly difficult to ensure the data privacy and integrity. That is, digital
imaging devices in the medical platform, patients’ health-related information storage on cloud servers, and the
information sharing among experts using public transmission networks, all would be possibly exposed or leaked. Further,
the volume and diversity of medical data also make privacy protection difficult. More precisely, healthcare systems
typically generate large amounts of data, including patient records, medical images, and genetic information, making it
difficult to ensure consistent privacy protection across the digital healthcare system.

Nowadays, swarm intelligence has emerged to collect, store, analyze and regulate the information of each security
component in healthcare systems. It is well acknowledged that swarm intelligence is efficient when it comes to data
processing and resource utilization in the decentralized and autonomous computing environments. Using the security
proactive prevention technology based on swarm intelligence, potential attacks can be detected. In particular, swarm
intelligence is built on a series of simple individuals with limited abilities, yet can accomplish complex security
protecting tasks. Since swarm intelligence algorithm has high flexibility and is naturally distributed with strong
robustness, they can be promisingly used in the data security of healthcare systems.

The aim of this special issue is to attract a combination of research articles that will highlight the use of swarm
intelligence in cybersecurity, especially in security and privacy domains, to spread the awareness about the adoption and
practices of swarm intelligence for cybersecurity techniques that can help the healthcare community. The topics of
interest for this special issue include but not limited to:

• Swarm intelligence for providing security, integrity, and privacy solutions for digital healthcare systems
• Energy-aware solutions based on swarm intelligence for secure digital healthcare systems
• Message authentication techniques based on swarm intelligence for digital healthcare systems
• Swarm intelligence-based techniques to provide hardware security in digital healthcare systems
• Intrusion detection and prevention using swarm intelligence in digital healthcare systems
• Authentication and authorization using swarm intelligence in digital healthcare systems
• Low power swarm intelligence-based techniques for securing digital healthcare systems
• New cryptographic algorithms combined with swarm intelligence for the security and privacy of digital healthcare
systems
• Providing security and privacy services for online data using swarm intelligence in digital healthcare systems
• Swarm intelligence-based communication protocols for securing communication between digital healthcare system

Guest Editors
Ming Xiao, KTH Royal Institute of Technology, mingx@kth.se
Giancarlo Fortino, University of Calabria, giancarlo.fortino@unical.it
Mian Ahmad Jan, University of Sharjah, mjan@sharjah.ac.ae
Lei Liu, Xidian University, leiliu@xidian.edu.cn

Key Dates
Deadline for Submission: 01 Jan., 2025
First Reviews Due: 01 Mar., 2025
Revised Manuscript Due: 01 May, 2025
Final Decision: 01 Jul., 2025

Special Issue on Fuzzy Deep Learning for Big Data Management in Healthcare
Submission Date: 2025-01-15

Fuzzy deep learning (FDL) has emerged as a powerful tool due to its ability to manage Big Data in Healthcare. The blending of fuzzy logic principles, which accommodate uncertainty, imprecision, and inherent ambiguity in medical data, and deep learning, the approach used to extract and analyze hidden patterns and insights of Big Data in Healthcare, addresses the inherent challenges for extensive and complex Healthcare datasets. This synergy enables more accurate diagnosis predictions, optimized information processing, and efficient decision- making, enhancing healthcare Big Data management's efficiency and efficacy, helping speed up patient recovery, and advancing the overall quality of the healthcare system. Despite its many advantages, implementing FDL in Big Data management in Healthcare encompasses several challenges. Interpreting the reasoning of FDL models is one of the paramount concerns as the architecture of
such models is very complex, producing non-availability of understandable path leading to poor acceptability in the
medical community. Training models based on FDL also need high computing power and resources, another pivotal
concern. Furthermore, the scarcity of labeled datasets is another concern, which adds noise during the model's training,
degrading the model's performance. Conserving ethical considerations about patient data privacy and security is another
critical challenge. Handling these challenges is crucial to harnessing FDL for Big Data management to transform the
healthcare sector.

This Special Issue will explore the application of Fuzzy Deep Learning in Big Data Management in healthcare. With the
explosive growth of healthcare data, effectively managing and analyzing such data has become a significant challenge.
Fuzzy Deep Learning offers a new solution to this challenge by building and training deep learning models to process
and analyze large-scale, high-dimensional, and complex healthcare data..

Topics of interest include, but are not limited to, the following:
• FDL for handling uncertainties and imprecision for Healthcare Big Data
• Improved Diagnostic Model for Fuzzy Big Data Efficient Decision Support Systems for Clinical Big Data Medical Big Data image analysis using FDL
• Predicting disease progression using FDL-powered time series massive data FDL-empowered data cleaning algorithms for medical Big Data FDL for Big Data Warehouse and Clustering
• Efficient FDL-supported Big Data farmwork
• Synergy of IoT and FDL for real-time Healthcare Big Data FDL-enabled Data Fusion algorithms for complex Big Data set
• FDL empowered Natural Language Processing for the interpretability of medical information
• Data Privacy and Security Algorithm for FDL-powered Big Data Accurate Algorithm for Predicting Patient Risk Prediction.

Guest Editors
Ke Wang, the Engineering Research Center of Trustworthy AI, Ministry of Education, wangke@jnu.edu.cn
Ihsan Ali, Southeast Missouri State University, ihsanali@ieee.org
Muhammad Khalid, University of Hull, mkhalid@kfupm.edu.sa
Xueyan Gong, Jinan University, xygong@jnu.edu.cn
Kuo-Hui Yeh, National Yang Ming Chiao Tung University, khyeh@gms.ndhu.edu.tw

Key Dates
Deadline for Submission: 15 Jan, 2025
First Reviews Due: 20 March, 2025
Revised Manuscript Due: 01 May, 2025
Final Decision: 30 June, 2025

Special Issue on Artificial Intelligence-enabled translational mental healthcare and cognitive neuroscience
Submission Date: 2025-05-30

Mental health and neurological disorders are significant global health concerns, and the complexity of these disorders
necessitates innovative approaches for diagnosis and treatment. AI technology has a promising role for the
transformation of mental healthcare and its pitfalls. The integration of AI-based computational techniques with the
cognitive science offers promising solutions to address these challenges. Given the increasing prevalence of these
disorders and the growing interest in computational neuroscience, this special issue is both important and timely.
Currently, the world faces a critical transformation in the fourth industrial age, called digital revolution, that distinguished
by the integration of different technology types. Accordingly, this special issue aims to bridge the gap between AI and
cognitive neuroscience to facilitate the translation toward the clinical practice applications. It fills the gap in the current
coverage of other related journals by focusing specifically on the application of computational approaches to mental
healthcare and neurological disorders. While existing literature may touch on aspects of these topics, this special issue
provides a comprehensive overview of the latest advancements and methodologies in this area, thereby complementing
and expanding the existing body of knowledge with highlighting the impactful role of AI in clinical practice for mental
healthcare.

This special issue aligns closely with the focus of the JBHI on Cognitive Neuroscience, which emphasizes understanding
of the brain function and dysfunction. By showcasing the latest advances in AI and the computational approaches for
mental healthcare and neurological disorders services, it contributes to the journal's mission of advancing knowledge in
cognitive neuroscience and related fields.

Topics of interest include, but are not limited to, the following:
• AI-based cognitive neuroscience
• Cognitive dysfunction in depression
• Medical image processing of neuroimaging
• Personalized treatment planning in epilepsy
• Data analytics for diagnosis of neurological disease
• Computational techniques for early prognosis of schizophrenia
• Predictive modelling for early detection of mental health disorders
• Artificial Intelligent in Alzheimer’s disease detection and prediction
• Implementation of AI in cognitive neuroscience and mental healthcare
• Computational approaches for cognitive deficits in Alzheimer's disease
• Computational analysis to identify neural signatures in bipolar disorder
• Computational models of anxiety disorders for improved understanding
• Artificial intelligence for big data analysis in mental health applications
• Computational tools for personalized diagnosis in neurological disorders
• Artificial intelligence in prediction and diagnosis of neurological disorders
• Computational neuroimaging analysis for biomarkers in psychiatric conditions
• Computational approaches for Parkinson's disease-related cognitive impairments

Guest Editors
Deepika Koundal, UPES Dehradun dkoundal@ddn.upes.ac.in
Amira S. Ashour, Tanta University, Egypt Amira.salah@f-eng.tanta.edu.eg
Yanhui Guo, University of Illinois, US yguo56@uis.edu
Mohit Mittal, Knowtion GmbH, Karlsruhe, Germany mohitmittal@ieee.org

Key Dates
Deadline for Submission: 30 May, 2025
First Reviews Due: 05 July, 2025
Revised Manuscript Due: 20 September 2025
Final Decision: 15 November, 2025

Special Issue on Contactless Human Sensing using Wireless Signals for Personalized Biomedical and Healthcare
Submission Date: 2025-05-31

Wireless sensing technology utilizes electromagnetic waves to perceive human individuals, providing accurate and
comprehensive human-related information for fields such as medical diagnosis and biological research. It offers new
possibilities for early disease diagnosis, treatment monitoring, and health assessment, addressing fundamental and
critical issues in biomedical and related fields. Meanwhile, with the gradual implementation of Integrated Sensing and
Communication (ISAC) in the fifth-generation-advanced (5G-A) and sixth-generation (6G) multi-functional networks,
using wireless signals, especially cellular signals, for contactless human sensing will become a general trend.
Furthermore, the radio communication division of the International Telecommunication Union (ITU-R) has recently
adopted ISAC among the key usage scenarios for IMT-2030/6G, making wireless human sensing technology more
indispensable in the coming 6G era. While current wireless human sensing has made significant strides, there is still
some way to go before its ubiquitous application and promotion. Some emerging applications for personalized
biomedical and healthcare, such as integrated wireless human sensing with telehealthcare, integrated communications
and wireless human sensing, as well as privacy and security issues in wireless human sensing, are also essential.
The main objective of this Special Issue is to address the unique challenges of wireless human sensing for personalized
biomedical and healthcare, and to bring contactless human sensing technique closer to reality. It will focus on various
theoretical and practical research on human sensing using wireless signals, aiming at bringing together researchers,
industry practitioners, and individuals working in related areas to share their new ideas, latest findings, and state-of-the-
art results. Ultimately, this special issue will provide a comprehensive tutorial of the state-of-the-art wireless human
sensing technologies for biomedical and healthcare.

Topics of interest include, but are not limited to, the following:
• Wireless human motion recognition approaches for personalized biomedical and healthcare
• Human imaging based on wireless signals for biomedical and healthcare
• Applications of artificial intelligence (AI) for wireless human sensing
• Human vital-sign (e.g., heartbeat and respiration) sensing for biomedical and healthcare
• Multi-source signal integration for contactless human sensing
• Human sensing datasets based on wireless signals for biomedical and healthcare
• Integrated wireless human sensing with telehealthcare
• Integrated communications and wireless human sensing for 6G
• Information security in personalized biomedical and healthcare based on wireless human sensing
• Experimental demonstrations and prototypes of wireless human sensing for personalized biomedical and healthcare

Guest Editors
Yuanhao Cui, Southern University of Science and Technology, cuiyuanhao@bupt.edu.cn
George C. Alexandropoulos, National and Kapodistrian University of Athens, alexandg@di.uoa.gr
Xinyu Li, Southeast University, xinyuli@seu.edu.cn
Jun Luo, Nanyang Technological University, junluo@ntu.edu.sg

Key Dates
Deadline for Submission: 31 May, 2025
First Reviews Due: 05 July, 2025
Revised Manuscript Due: 20 August, 2025
Final Decision: 31 September, 2025

Special Issue on Novel applications of Language Model Technologies in disease diagnosis
Submission Date: 2025-05-31

In recent years, the convergence of machine intelligence and healthcare has paved the way for significant advances in
disease diagnosis, particularly emphasizing the use of Language Model Technologies (LMTs). This special issue strives
to untangle the complications surrounding the use of LMTs in medical diagnostics, shedding light on the challenges
researchers and practitioners face in harnessing the full potential of these innovative tools. From deciphering intricate
medical data to enhancing diagnostic accuracy, LMTs present a myriad of opportunities that this issue seeks to explore
comprehensively. Additionally, the special edition emphasizes the imperative of fostering patient-centric healthcare
through the seamless integration of these technologies, ensuring that the diagnostic process becomes more efficient,
personalized, and empathetic. As we delve into the articles within this issue, we anticipate a profound understanding of
the current landscape, prospects, and the transformative impact that LMTs can have on revolutionizing disease diagnosis
and healthcare delivery.

However, integrating Language Model Technologies in smart diagnosis involves challenges. Working with unbalanced
data, feature engineering, data privacy concerns, and the interpretability of AI-driven diagnoses pose significant hurdles
that necessitate careful evaluation. This special focus seeks to address these intricacies. Opportunities abound in the
refinement of LMTs for tailored, patient-centric healthcare solutions, where the focus is not only on accurate diagnosis
but also on providing meaningful and understandable information to patients. The integration of LMTs promises to create
a more inclusive and empathetic healthcare ecosystem. As we navigate the technical intricacies of deploying LMTs in
smart diagnosis, it becomes imperative to strike a balance between innovation and ethical considerations to ensure that
the transformative potential of these technologies is harnessed responsibly for the betterment of patient outcomes and
overall healthcare delivery.

Topics of interest include, but are not limited to, the following:
• Novel applications of LMTs in disease diagnosis.
• Challenges and ethical considerations in deploying LMTs for medical purposes.
• Integration of LMTs with existing diagnostic tools and technologies.
• Patient-centric approaches in healthcare through LMT-enhanced diagnostics.
• Impact of LMTs on diagnostic accuracy and efficiency.
• Ensuring transparency in AI-driven diagnoses.
• Interdisciplinary perspectives on the convergence of healthcare and artificial intelligence.
• Ambient assisted living with LMTs for better livelihood.
• Harnessing wearable devices and real-time health data through LMTs.
• Case studies on the application of LMTs in smart diagnosis.

Guest Editors
Prof. Marcin Woźniak, Silesian University of Technology, Gliwice, Poland, marcin.wozniak@polsl.pl
Dr. Muhammad Fazal Ijaz, Melbourne Institute of Technology, Melbourne, Australia, mfazal@mit.edu.au
Dr. Mohit Mittal, Knowtion GmbH, Karlsruhe, Germany mohitmittal@ieee.org
Prof. Neal N. Xiong, Sul Ross State University, Alpine, USA, neal.xiong@sulross.edu

Key Dates
Deadline for Submission: 31 May, 2025
First Reviews Due: 05 July, 2025
Revised Manuscript Due: 05 August, 2025
Final Decision: 05 September, 2025