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“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.”

“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.”

“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.”

“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.”

“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.”

“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.”

“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.”