Latest Issue

    Volume 40 Issue 1 2025

      Editorial

    • Strengthening Biomedical Big Data Management and Unleashing the Value of Data Elements AI Introduction

      In the field of xxx, expert xx has made significant research progress. By establishing the xx system/exploring the xx topic/verifying the xx conjecture, xx has provided solutions to solve xx problems/open up a new direction for xx research/lay a foundation for the construction of the xx system.
      Wei Zhou, Jing-Chen Zhang, De-Pei Liu
      Vol. 40, Issue 1, Pages: 1-2(2025) DOI: 10.24920/004471
        
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      Perspective

    • Evaluation and Regulation of Medical Artificial Intelligence Applications in China AI Introduction

      In the digital economy era, China's medical AI applications are advancing rapidly. The China National Health Development Research Center has established a value assessment framework for AI medical technologies, formulated the country's first technical guideline for clinical evaluation, and validated their practicality through scenario-based pilot studies. The paper also proposes introducing a "regulatory sandbox" model to test technical compliance in controlled environments, thereby balancing innovation incentives with risk governance.
      Mao You, Yue Xiao, Han Yao, Xue-Qing Tian, Li-Wei Shi, Ying-Peng Qiu
      Vol. 40, Issue 1, Pages: 3-8(2025) DOI: 10.24920/004473
      Evaluation and Regulation of Medical Artificial Intelligence Applications in China
      Abstract:Amid the global wave of digital economy, China's medical artificial intelligence applications are rapidly advancing through technological innovation and policy support, while facing multifaceted evaluation and regulatory challenges. The dynamic algorithm evolution undermines the consistency of assessment criteria, multimodal systems lack unified evaluation metrics, and conflicts persist between data sharing and privacy protection. To address these issues, the China National Health Development Research Center has established a value assessment framework for artificial intelligence medical technologies, formulated the country's first technical guideline for clinical evaluation, and validated their practicality through scenario-based pilot studies. Furthermore, this paper proposes introducing a "regulatory sandbox" model to test technical compliance in controlled environments, thereby balancing innovation incentives with risk governance.  
      Keywords:regulatory sandbox;medical artificial intelligence;health technology assessment   
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      Review Article

    • Biomedical Data in China: Policy, Accumulation, Platform Construction, and Applications AI Introduction

      In the era of big data, biomedical data is growing rapidly, presenting challenges in data management. This article summarizes China's policies, data collection, platform construction, and applications in biomedical data, aiming to identify key issues and needs, enhance platform construction capacity, unleash data value, and leverage China's vast data advantages.
      Jing-Chen Zhang, Jing-Wen Sun, Xiao-Meng Liu, Jin-Yan Liu, Wei Luo, Sheng-Fa Zhang, Wei Zhou
      Vol. 40, Issue 1, Pages: 9-17(2025) DOI: 10.24920/004445
      Biomedical Data in China: Policy, Accumulation, Platform Construction, and Applications
      Abstract:Biomedical data is surging due to technological innovations and integration of multidisciplinary data, posing challenges to data management. This article summarizes the policies, data collection efforts, platform construction, and applications of biomedical data in China, aiming to identify key issues and needs, enhance the capacity-building of platform construction, unleash the value of data, and leverage the advantages of China's vast amount of data.  
      Keywords:biomedical data;data management;database;data sharing;data resources;data platform   
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    • Data Spaces in Medicine and Health: Technologies, Applications, and Challenges AI Introduction

      In the medical and health sectors, data space is emerging as an innovative model for data management and sharing. This study introduces its research progress in the field of data space, which provides solutions to solve the challenges of limited computing resources, data integration complexities, and the need for optimized algorithms. Technological innovation, sound regulations, and optimized management will help the development of the medical and health data space, enabling the secure and efficient utilization of data.
      Wan-Fei Hu, Si-Zhu Wu, Qing Qian
      Vol. 40, Issue 1, Pages: 18-28(2025) DOI: 10.24920/004466
      Data Spaces in Medicine and Health: Technologies, Applications, and Challenges
      Abstract:Data space, as an innovative data management and sharing model, is emerging in the medical and health sectors. This study expounds on the conceptual connotation of data space and delineates its key technologies, including distributed data storage, standardization and interoperability of data sharing, data security and privacy protection, data analysis and mining, and data space assessment. By analyzing the real-world cases of data spaces within medicine and health, this study compares the similarities and differences across various dimensions such as purpose, architecture, data interoperability, and privacy protection. Meanwhile, data spaces in these fields are challenged by the limited computing resources, the complexities of data integration, and the need for optimized algorithms. Additionally, legal and ethical issues such as unclear data ownership, undefined usage rights, risks associated with privacy protection need to be addressed. The study notes organizational and management difficulties, calling for enhancements in governance framework, data sharing mechanisms, and value assessment systems. In the future, technological innovation, sound regulations, and optimized management will help the development of the medical and health data space. These developments will enable the secure and efficient utilization of data, propelling the medical industry into an era characterized by precision, intelligence, and personalization.  
      Keywords:data space;medical and health data;data sharing;privacy protection;data security   
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    • In the field of viral infectious diseases, this review explores the multifaceted nature of the diseases and summarizes relevant data across multiple levels. It traces the historical evolution of data collection, evaluates current limitations, and proposes strategies to surmount challenges, focusing on advanced computational techniques, AI-driven models, and enhanced data integration practices.
      Yun Ma, Lu-Yao Qin, Xiao Ding, Ai-Ping Wu
      Vol. 40, Issue 1, Pages: 29-44(2025) DOI: 10.24920/004461
      Diversity, Complexity, and Challenges of Viral Infectious Disease Data in the Big Data Era: A Comprehensive Review
      Abstract:Viral infectious diseases, characterized by their intricate nature and wide-ranging diversity, pose substantial challenges in the domain of data management. The vast volume of data generated by these diseases, spanning from the molecular mechanisms within cells to large-scale epidemiological patterns, has surpassed the capabilities of traditional analytical methods. In the era of artificial intelligence (AI) and big data, there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information. Despite the rapid accumulation of data associated with viral infections, the lack of a comprehensive framework for integrating, selecting, and analyzing these datasets has left numerous researchers uncertain about which data to select, how to access it, and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels, from the molecular details of pathogens to broad epidemiological trends. The scope extends from the micro-scale to the macro-scale, encompassing pathogens, hosts, and vectors. In addition to data summarization, this review thoroughly investigates various dataset sources. It also traces the historical evolution of data collection in the field of viral infectious diseases, highlighting the progress achieved over time. Simultaneously, it evaluates the current limitations that impede data utilization.Furthermore, we propose strategies to surmount these challenges, focusing on the development and application of advanced computational techniques, AI-driven models, and enhanced data integration practices. By providing a comprehensive synthesis of existing knowledge, this review is designed to guide future research and contribute to more informed approaches in the surveillance, prevention, and control of viral infectious diseases, particularly within the context of the expanding big-data landscape.  
      Keywords:viral infectious diseases;big data;data diversity and complexity;data standardization;artificial intelligence;data analysis   
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    • Enrichment Analysis and Deep Learning in Biomedical Ontology: Applications and Advancements AI Introduction

      Biomedical big data, with its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
      Hong-Yu Fu, Yang-Yang Liu, Mei-Yi Zhang, Hai-Xiu Yang
      Vol. 40, Issue 1, Pages: 45-56(2025) DOI: 10.24920/004464
      Enrichment Analysis and Deep Learning in Biomedical Ontology: Applications and Advancements
      Abstract:Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.  
      Keywords:biomedical ontology;enrichment analysis;deep learning;ontology hierarchy;ontology annotation   
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      Research Article

    • Formula-S: Situated Visualization for Traditional Chinese Medicine Formula Learning Enhanced Publication AI Introduction

      In the field of traditional Chinese medicine (TCM) education, researchers have introduced Formula-S, a situated visualization method for TCM formula learning in augmented reality (AR). This interactive AR tool, featuring three modes (3D, Web, and Table), provides solutions to enhance TCM formula learning for beginners, offering more efficient and accurate searching capabilities compared to traditional and web-based methods.
      Zhi-Yue Wu, Su-Yuan Peng, Yan Zhu, Liang Zhou
      Vol. 40, Issue 1, Pages: 57-67(2025) DOI: 10.24920/004462
      Formula-S: Situated Visualization for Traditional Chinese Medicine Formula Learning
      Abstract:ObjectiveThe study of medicine formulas is a core component of traditional Chinese medicine (TCM), yet traditional learning methods often lack interactivity and contextual understanding, making it challenging for beginners to grasp the intricate composition rules of formulas. To address this gap, we introduce Formula-S, a situated visualization method for TCM formula learning in augmented reality (AR) and evaluate its performance. This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.MethodsFormula-S is an interactive AR tool designed for TCM formula learning, featuring three modes (3D, Web, and Table). The dataset included TCM formulas and herb properties extracted from authoritative references, including textbook and the SymMap database. In Formula-S, the hierarchical visualization of the formulas as herbal medicine compositions, is linked to the multidimensional herb attribute visualization and embedded in the real world, where real herb samples are presented. To evaluate its effectiveness, a controlled study (n=30) was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification, formula composition, and recognition. In the study, participants interacted with the AR tool through HoloLens 2. Data were collected on both task performance (accuracy and response time) and user experience, with a focus on task efficiency, accuracy, and user preference across the different learning modes. Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks. Regarding user experience, our new approach demonstrated the highest system usability and lowest task load, effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency. Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment, suggesting this approach offers usability advantages for TCM education.ConclusionsThe situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods. Additionally, it provides superior contextual understanding of TCM formulas, making it a promising new solution for TCM learning.  
      Keywords:health informatics;situated visualization augmented reality;traditional Chinese medicine;formula   
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    • scPANDA: PAN-Blood Data Annotator with a 10-Million Single-Cell Atlas Enhanced Publication AI Introduction

      In the field of single-cell RNA sequencing, researchers have developed the PAN-blood single-cell Data Annotator (scPANDA) system, which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation. This system exemplifies effective reference mapping with a large-scale atlas, enhancing the accuracy and reliability of blood cell type identification.
      Chang-Xiao Li, Can Huang, Dong-Sheng Chen
      Vol. 40, Issue 1, Pages: 68-87(2025) DOI: 10.24920/004472
      scPANDA: PAN-Blood Data Annotator with a 10-Million Single-Cell Atlas
      Abstract:ObjectiveRecent advancements in single-cell RNA sequencing (scRNA-seq) have revolutionized the study of cellular heterogeneity, particularly within the hematological system. However, accurately annotating cell types remains challenging due to the complexity of immune cells. To address this challenge, we develop a PAN-blood single-cell Data Annotator (scPANDA), which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation.MethodsThe atlas, constructed from data collected in 16 studies, incorporated rigorous quality control, preprocessing, and integration steps to ensure a high-quality reference for annotation. scPANDA utilizes a three-layer inference approach, progressively refining cell types from broad compartments to specific clusters. Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis. Furthermore, the performance of scPANDA was evaluated in three external datasets.ResultsThe atlas was structured hierarchically, consisting of 16 compartments, 54 classes, 4,‍460 low-level clusters (pd_cc_cl_tfs), and 611 high-level clusters (pmid_cts). Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets, analyzing immune-tumor coexisting clusters in renal cell carcinoma, and identifying conserved cell clusters across species.ConclusionscPANDA exemplifies effective reference mapping with a large-scale atlas, enhancing the accuracy and reliability of blood cell type identification.  
      Keywords:single-cell RNA sequencing;immunology;cell type annotation;single-cell atlas;blood cells   
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