J-BHI 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.

Specific topics covered by J-BHI include but are not limited to:

    Sensor Informatics – body sensor networks; wearable, ingestible and implantable systems; biosensor design, miniaturisation and embodiment; antennas, RF and intra-body communication; wireless communication standards, security and privacy, and QoS; smart point-of-care and wireless physiological monitoring devices (e.g. ECG, EMG, EEG and PPG); ASIC and embedded system design, on-node processing, smart devices and app development; autonomic sensing, distributed inference, context aware sensing and multi-sensor fusion; data compression; wearable and assistive devices for rehabilitation, well-being and ageing population;
    Bioinformatics – biological information systems and large scale ‘-omics’ databases, modelling, pattern matching and algorithms; systems biology, computational biology, and organ/system/disease-level informatics (e.g. physiome, neuro-informatics, cancer-informatics, and cardiovascular-informatics platforms);
    Imaging Informatics – image-enabled electronic medical records (EMR), PACS, hospital information systems (HIS), and radiology information systems (RIS) integration; computer aided diagnosis, database aggregation and distribution; image biomarkers and high-throughput systems; metadata, ontology and content-based image retrieval and decision support; multi-scale image fusion, visualization, high-content screening and image data mining;
    Medical Informatics – electronic health record, interoperability, connectivity, semantics, syntax, vocabulary, terminology, ontologies, standards, clinical guidelines/pathways, protocols; privacy, security, trust, PKI, smart card technology; regional and community health information networks, hospital information systems and clinical information systems, including PACS and disease management systems; collaborative technologies in health care delivery, including telemedicine; intelligent interpretation of health data, decision support systems, computer assisted, remote guidance and virtual reality applications in diagnostic and therapeutic procedures.
    Public Health Informatics – pervasive healthcare, wellness management utilising, e.g., ubiquitous computing, smart environments, embedded and wearable sensors; influenza monitoring and prediction; life-style chronic disease management and behaviour modification; personalized and pervasive health technologies (telemedicine, u-, p-, m- and e-Health) for public health.
    Modeling and AI in Informatics – Multiscale modeling molecular/cellular/tissue/organ/system/disease-level informatics, neuro-informatics, cancer-informatics, and cardiovascular-informatics platforms); data science and data engineering for biomedicine and health; network mining and modeling; biomedical and health data curation, augmentation and harmonization; machine learning and artificial intelligence methodologies for biomedical data analysis and interpretation; intelligent decision support systems for improving health outcomes; and intelligent informatics for extended digital health reality.

In the topics of Sensor Informatics, Imaging Informatics, Medical Informatics and Public Health Informatics only human studies are allowed, while in the area of Bioinformatics both human and animal studies are allowed.

Special Issue on Digital Twins for Precision Medicine and Patient-Centered Healthcare
Submission Date: 2025-10-30

Digital Twins (DTs) in precision healthcare represent dynamic, real-time virtual replicas of individual patients, integrating multimodal data streams such as EHRs, genomic profiles, wearable sensor data, and environmental context. The figure illustrates a layered DT architecture comprising patient data acquisition (via IoT/wearables), AI-driven analytics and simulation layers, and a secure decision-making module that assists clinicians in diagnosis, treatment planning, and remote monitoring. This system enables continuous feedback loops between the physical and virtual domains, empowering predictive modelling, early diagnosis, personalized therapies, and proactive intervention. Emphasizing interoperability, security, and patient-centered care, this framework aligns closely with the objectives of the Special Issue, showcasing how digital twins can transform healthcare delivery.

Special Issue on 6G-Technology-Driven Healthcare Informatics
Submission Date: 2025-10-31

The special issue on “6G-Technology-Driven Healthcare Informatics” invites pioneering research that leverages 6G communication technologies to enhance biomedical and health informatics. As 6G introduces breakthroughs such as ultra-low latency, intelligent edge computing, terahertz communication, and quantum-secured data exchange, this issue explores their applications in remote surgery, AI-assisted diagnostics, federated learning, and the integration of metaverse-based virtual healthcare systems. Researchers are encouraged to contribute innovative methods that align with the future of personalized, secure, and connected healthcare systems.

Special Issue on Transparent Large Vision-Language Models in Healthcare
Submission Date: 2025-10-31

This Special Issue addresses the critical need for transparency in large-scale Vision–Language Models (VLMs) applied to healthcare. As VLMs integrate multimodal clinical data—including radiology and pathology images, free-text clinical notes, and structured electronic health records—they enhance diagnostic and prognostic accuracy. However, challenges such as model opacity, interpretability gaps, and regulatory compliance remain significant. This collection highlights innovative methodologies, system architectures, and transparency frameworks that enable explainable, auditable, and clinically trustworthy vision–language AI systems.

Special Issue on Efficient Deep Learning Frontiers for Next-Generation Biomedical Sensing Intelligence
Submission Date: 2025-11-30

The rapid advancement of sensing technologies has revolutionized biomedical and health informatics by enabling continuous physiological and behavioral data collection. However, the widespread deployment of AI remains constrained by large annotation requirements and inherent class imbalance in biomedical data. This special issue, dedicated to next-generation biomedical sensing intelligence, invites cutting-edge research on data- and label-efficient learning methods—such as self-supervised, semi-supervised, and few-shot learning, as well as efficient training/deployment paradigms for large foundation models—to drive the development of deep learning technologies specifically tailored for biomedical sensing informatics.

Special Issue on Integrity of Medical Data: From Capture to Use
Submission Date: 2025-11-30

Due to smart healthcare system is highly connected to advanced wearable devices, internet of things (IoT) and mobile internet, valuable patient information and other significant medical records are easily transmitted over the public network. The personal patient information and clinical records are also stored on the existing databases and local servers of the hospital and healthcare centres. This information not only provide a reference for healthcare professionals to make correct decision on the patient, but also provide basis for other professionals to make effective treatment and develop future plans for correct diagnosis. Further, the databases may be used by various research communities for different directions of research, without the any possibility of privacy violations. However, stealing of healthcare data is growing crime every day to greatly impact on financial loss. Aiming to guarantee the security and privacy of patient record in the transfer process, the integrity authentication of them is extremely important. Therefore, proper medical data security is becoming equally important in smart healthcare.

Special Issue on Artificial Intelligence-enabled translational mental healthcare and cognitive neuroscience
Submission Date: 2025-12-30

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.

Special Issue on Orchestrating Biomedical Breakthroughs through the Fusion of NLP Techniques and GPT Transformers
Submission Date: 2025-12-31

The integration of natural language processing (NLP) techniques with GPT (Generative Pre-trained Transformer) models offers immense potential for biomedical applications. GPT models possess remarkable language generation and contextual comprehension abilities, which, when combined with NLP, can enhance a wide array of biomedical tasks. This integration facilitates tasks such as biomedical text generation, medical question-answering systems, and clinical decision support, benefiting both healthcare professionals and researchers. Nonetheless, challenges include the scarcity of high-quality biomedical data for model fine-tuning, the need for continuous model updates due to evolving medical knowledge, and ensuring model interpretability, transparency, and ethical considerations. This special issue seeks to address these challenges and welcomes contributions encompassing experimental, conceptual, and theoretical approaches to advance the field of biomedical applications.

Special Issue on The cutting-edge artificial intelligence techniques and their applications in drug discovery, part 2
Submission Date: 2025-12-31

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. Given the significance of these developments, it is imperative to dedicate a special issue to the exploration of new AI technologies in the field of drug discovery. This special issue aims to showcase the latest advancements in AI-driven methodologies, highlighting their applications, challenges, and prospects in pharmaceutical research.

Special Issue on Revolutionizing Intelligent Disease Diagnosis: Generative Medical Image Processing, Evaluation, and Application
Submission Date: 2025-12-31

Recent advances in generative artificial intelligence (AI) are transforming the landscape of biomedical imaging, particularly in the context of intelligent disease diagnosis. With the rise of generative AI models, it has become increasingly feasible to synthesize medical images that exhibit high visual and structural fidelity with respect to real patient data. However, the integration of synthetic medical images into clinical workflows and diagnostic systems raises critical questions about quality, reliability, and clinical utility. Moreover, the medical imaging community currently lacks standardized protocols for assessing both the visual realism and diagnostic utility of synthetic images. This special issue aims to bring together researchers, clinicians, data scientists, and ethicists to explore this frontier at the intersection of medical image generation, quality evaluation, and diagnostic application.

Special Issue on Application of computational techniques in drug discovery and disease treatment, Part III
Submission Date: 2025-12-31

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.

Special Issue on Explainable AI-Driven Medical Imaging for Cancer Diagnosis and Treatment Planning
Submission Date: 2026-01-20

The integration of explainable artificial intelligence (XAI) with medical imaging is revolutionizing cancer diagnosis and treatment planning by enabling transparent, interpretable, and clinically reliable decision-making. This approach prioritizes model interpretability, decision traceability, and clinical trust through the use of interpretable neural architectures such as attention-based U-Nets and Vision Transformers, along with uncertainty-aware learning and saliency-based visualization. Furthermore, outcome-driven treatment response prediction, causal inference frameworks, and domain adaptation strategies (e.g., transfer learning, few-shot learning) enhance model generalizability. Emphasis on uncertainty quantification, SHAP/LIME-based explanations, and model calibration supports regulatory compliance and integration with clinical decision support systems, ultimately bridging the gap between black-box AI models and real-world precision oncology applications.

Special Issue on Role of AI and Explainable AI in Integrative Approaches for Healthcare Data Analysis
Submission Date: 2026-01-31

An Artificial Intelligence (AI) and Explainable AI (XAI) based smart healthcare ecosystem is an advanced framework integrating multi-model data analysis, transparent decision-making processes, and ethical medical informatics. In AI-driven healthcare systems, diverse data sources such as electronic health records (EHRs), genomic profiles, wearable device outputs, and imaging data are seamlessly analyzed using deep learning, machine learning, and explainable models to ensure accurate, interpretable, and patient-centric healthcare delivery. Key components of this evolving ecosystem include multi-model AI techniques, explainable AI frameworks, healthcare data analytics, cloud-edge computing, cybersecurity protocols, and ethical AI governance. The synergy of AI and XAI not only enhances predictive capabilities and diagnostic accuracy but also promotes transparency, trust, and shared decision-making between healthcare providers and patients. The main objective of this Special Issue is to bring together innovative research contributions addressing technological advancements, ethical challenges, application frameworks, real-world deployments, and future directions in the integration of AI and XAI for transformative smart healthcare solutions.

Special Issue on Multimodal Medical Data-driven Biomedical and Health Informatics: Challenges and Solutions
Submission Date: 2026-01-31

This special issue will improve the comprehensive application of medical data by exploring multimodal medical data fusion methods and tools based on deep learning and AI; explore emerging multimodal data fusion model architectures, training techniques, and optimization methods to improve the accuracy of data fusion and reduce computational complexity; study methods for assessing privacy and security risks to ensure the safe and compliant use of medical data; study the application of bioinformatics computing methods and multimodal data analysis in health testing and disease management to provide strong support for the intelligent development of the medical field. In addition, disease diagnosis and personalized treatment based on medical multimodal big data, the bioinformatics foundation of future holographic intelligent medicine, and the practice of high-performance computing in bioinformatics are also important contents of this special issue.

Special Issue on Applying Federated Machine Learning Approaches for Human-Robot-Interaction
Submission Date: 2026-01-31

Human-robot communication, abbreviated as HRI, is the study of efficient and effective communication or interplay between humans and robots. HRI is a multidisciplinary idea usually utilized in areas including natural language processing, smart computer vision, robotics systems, artificial intelligence, and psychological studies. In the field of human-robot interactions with a focus on machine learning in its broadest sense to enable social interactions between humans and robots. This technology explores how vision, speech and sound, small movements, gestures, and user proximity affect the way we perceive and work with social robots.

Special Issue on Biomedical and Healthcare Intelligence Using Imperfect Data: Challenges, Advances and Trend
Submission Date: 2026-02-28

Artificial intelligence (AI), especially deep learning, has greatly hastened the modern medical diagnostics, particularly the interpretation of medical data. For instance, AI assists to locate lesion areas from medical images, detect heart disease from electrocardiograms, analyze brain disease from electroencephalogram etc. Generally, the performance of AI models in medical diagnostics heavily depends on the labeled medical data. However, the collected medical data is usually imperfect, insufficient, imbalanced, or incorrectly labeled. It is time-consuming, expensive, and laborious to collect a large scale of high quality labeled medical data. How to use imperfect data for making precise diagnoses is a vital challenge in the field of biomedical and healthcare intelligence. In order to handle this challenge and facilitate smart healthcare, this special issue will focus on the challenges, advances, and trend of weakly supervised learning on biomedical and healthcare intelligence. Compared with traditional supervised learning, weakly supervised learning focuses on learning under incomplete supervision, inexact supervision, or inaccurate supervision. Medical data is different from classical machine learning data. Generally, medical data has special concerns which need doctors or researchers to pay attention. Therefore, it is critical to explore suitable weakly supervised learning model for achieving the best medical diagnoses in the biomedical and healthcare intelligence.

Special Issue on Harnessing Cutting-Edge Privacy Technologies to Address Data Security Challenges in Healthcare Informatics
Submission Date: 2026-03-06

This Special Issue aims to advance privacy-preserving technologies that enable secure and effective use of complex, multi-modal healthcare data. Topics include federated learning encrypted computation, differential privacy, and privacy-aware adaptations of large language models, with a focus on balancing data privacy with healthcare innovation.

Special Issue on Explainable AI 2.0 for Healthcare 4.0
Submission Date: 2026-03-31

Healthcare 4.0 uses AI, IoT, cloud computing, and cyber-physical systems to provide personalized, real-time medical services. Explainable AI (XAI) plays a pivotal role in developing trust and transparency in AI decisions. However, traditional XAI provides static explanations irrespective of the individual’s skill sets. Explainable AI 2.0 addresses this concern by providing context-sensitive and personalized explanations. However, implementing XAI 2.0 in Healthcare 4.0 poses severe concerns, including the trade-off between model performance and explainability, the complexity of unified models, and the need for true context-sensitive explanations. Additionally, lack of standard metrics to assess the effectiveness of XAI 2.0 systems and regulatory and ethical concerns for ensuring not only technical soundness but also legal defensibility are other paramount concerns. The aim of the special issue is to transform healthcare 4.0 through transparent, trustworthy, context-sensitive, personalized, and human-centric AI systems that enhance clinical decision-making.

Special Issue on Next Generation AI for Eye Healthcare — From Bench to Bedside
Submission Date: 2026-03-31

Recent advances in AI have revolutionized eye healthcare, creating unprecedented opportunities for academia, healthcare, and industry. Despite this progress, significant challenges persist, as ophthalmic data in real-world clinical settings are highly diverse, frequently incomplete, and often imperfect, with labels that may be scarce, noisy, or only partially available. There is a pressing need for novel AI methods capable of effectively handling such real-world data. This special issue seeks to unite researchers from a wide range of disciplines to present cutting-edge AI approaches for analysing incomplete and imperfect ophthalmic data, with the aim of advancing AI-driven eye healthcare from bench to bedside and ultimately enabling their translation into clinical practice.

Special Issue on Large Language Models with Applications in Bioinformatics and Biomedicine, Part II
Submission Date: 2026-06-01

This special issue is dedicated to examining the transformative role of advanced language models (LLMs) in bioinformatics and biomedicine. While LLMs have already revolutionized fields like natural language processing and computer vision, their potential within bioinformatics and biomedicine is only beginning to be realized. By focusing on their applications in areas such as drug discovery, genomics, transcriptomics, proteomics, and single-cell analysis, this issue seeks to address the current lack of focused coverage on this topic. The articles featured aim to highlight the advancements and possibilities that LLMs offer in navigating the complexities of multi-omics data and the intricate layers of human physiology, fostering a deeper understanding and promoting future innovations in these critical fields.

Special Issue on Explainable Artificial Intelligence and Cognitive Computation in the Learning of Human Physiological Systems
Submission Date: 2026-08-31

This Special Issue calls for work that integrates XAI with deep learning in physiology, prioritizing studies that: 1) Develop novel interpretability techniques tailored to physiological time-series data, 2) Validate model explanations against domain knowledge (e.g., through clinician-in-the-loop evaluations), or 3) Use XAI to derive testable biological hypotheses. By fostering collaboration between AI researchers and physiologists, we can advance not just predictive performance but also scientific understanding of human health and disease. The special issue will be comprised of extensions of some of the best works announced in the workshop, along with papers submitted within the open call, taking also into account the target audience of the JBHI journal.

Special Issue on Agentic AI for Healthcare: Towards Autonomous, Trustworthy, and Human-Centric Systems
Submission Date: 2026-08-31

This Special Issue will explore Agentic Artificial Intelligence (AI) in Healthcare, focusing on systems that move beyond prediction and assistance to proactive, autonomous, and goal-directed actions under human-defined constraints. By integrating multimodal biomedical data, medical imaging, sensor streams, and patient records, Agentic AI has the potential to deliver adaptive, context-aware, and personalized support for prevention, diagnosis, treatment, and long-term health monitoring. The issue welcomes contributions on architectures, frameworks, and applications of Agentic AI, as well as work addressing safety, transparency, ethical and regulatory considerations, and human-AI collaboration in clinical and home-care settings.