1.Artificial intelligence in stomatology: Innovations in clinical practice, research, education, and healthcare management.
Xuliang DENG ; Mingming XU ; Chenlin DU
Journal of Peking University(Health Sciences) 2025;57(5):821-826
In recent years, China has continued to face a high prevalence of oral diseases, along with uneven access to high-quality dental care. Against this backdrop, artificial intelligence (AI), as a data-driven, algorithm-supported, and model-centered technology system, has rapidly expanded its role in transforming the landscape of stomatology. This review summarizes recent advances in the application of AI in stomatology across clinical care, biomedical and materials research, education, and hospital management. In clinical settings, AI has improved diagnostic accuracy, streamlined treatment planning, and enhanced surgical precision and efficiency. In research, machine learning has accelerated the identification of disease biomarkers, deepened insights into the oral microbiome, and supported the development of novel biomaterials. In education, AI has enabled the construction of knowledge graphs, facilitated personalized learning, and powered simulation-based training, driving innovation in teaching methodologies. Meanwhile, in hospital operations, intelligent agents based on large language models (LLMs) have been widely deployed for intelligent triage, structured pre-consultations, automated clinical documentation, and quality control, contributing to more standardized and efficient healthcare delivery. Building on these foundations, a multi-agent collaborative framework centered around an AI assistant for stomatology is gradually emerging, integrating task-specific agents for imaging, treatment planning, surgical navigation, follow-up prediction, patient communication, and administrative coordination. Through shared interfaces and unified knowledge systems, these agents support seamless human-AI collaboration across the full continuum of care. Despite these achievements, the broader deployment of AI still faces challenges including data privacy, model robustness, cross-institution generalization, and interpretability. Addressing these issues will require the development of federated learning frameworks, multi-center validation, causal reasoning approaches, and strong ethical governance. With these foundations in place, AI is poised to move from a supportive tool to a trusted partner in advancing accessible, efficient, and high-quality stomatology services in China.
Artificial Intelligence
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Humans
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Oral Medicine/trends*
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China
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Delivery of Health Care
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Machine Learning
2.Inflammasome and diseases of central nervous system
Xiuming DU ; Ziteng ZHANG ; Ying ZONG ; Chenlin YU ; Xiaodong ZHANG ; Guocai LU
Journal of Pharmaceutical Practice 2016;(1):12-15
Inflammasome is the body of protein complex which is able to identify different stimulation signals and can in‐duce immune and inflammatory responses when it is activated .NLRP3 inflammasome has been extensively studied in the central nervous system .Microglia ,perivascular macrophages and meningeal macrophages express inflammasomes .The occurrence and development of acute brain infection ,aseptic acute brain injury ,Parkinson disease and Alzheimer disease are closely related to inflammasomes .Based on mechanisms exploration of the inflammasome′s role in the central nervous system disorders ,its tar‐get drug research and development will be greatly addressed in the future .

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