人工智能驱动口腔医学：临床、科研、教学与管理的创新探索

doi:10.19723/j.issn.1671-167X.2025.05.002

摘要/Abstract

摘要： 近年来，我国持续面临口腔疾病发病率居高不下、高质量口腔诊疗资源分布不均等突出问题。在此背景下，作为以数据驱动、算法支持和模型推理为核心的智能技术体系，人工智能(artificial intelligence，AI)在口腔医学中持续拓展其应用边界。为此，本文系统回顾了AI在口腔医学领域的最新应用进展，涵盖临床诊疗、基础与材料科学研究、医学教育和医院管理等多个关键环节。具体而言，在临床方面，AI显著提升了口腔疾病的诊断准确性、治疗方案的制定效率，以及手术操作的智能化水平；在科研方面，机器学习加快了口腔疾病生物标志物的识别、口腔微生态的解析与新型生物材料的研发进程；在教育方面，AI助力构建口腔医学知识图谱，推动个性化学习与虚拟仿真训练，促进教学模式的深度变革；在医院管理层面，基于大语言模型的智能体已广泛应用于智能分诊、预问诊生成、病历文书处理与质控审核，有效提升了医疗服务的规范化和精细化水平。在此基础上，一个以“AI口腔助手”为核心的多智能体协同体系正逐步构建，涵盖影像分析、治疗规划、术中导航、随访预测、患者沟通和行政管理等多个环节，通过统一接口与知识系统，实现覆盖诊疗全流程的人机协同。尽管AI已取得显著成效，其在实际应用中仍面临数据隐私保护、模型稳定性、跨机构适应性及可解释性等多重挑战。未来亟需构建联邦学习框架、开展多中心验证、引入因果推理方法并健全伦理治理体系，以保障AI在临床中的安全性与可用性。在上述基础上，AI有望从“辅助工具”迈向“可信伙伴”，以持续推动我国口腔医学服务体系向更加可及、高效与高质量的方向迈进。

关键词: 人工智能, 口腔医学, 深度学习, 智能体

Abstract: 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.

Key words: Artifical intelligence, Stomatology, Deep leaning, Agent

中图分类号:

邓旭亮, 徐明明, 杜宸临. 人工智能驱动口腔医学：临床、科研、教学与管理的创新探索[J]. 北京大学学报（医学版）, 2025, 57(5): 821-826.

Xuliang DENG, Mingming XU, Chenlin DU. Artificial intelligence in stomatology: Innovations in clinical practice, research, education, and healthcare management[J]. Journal of Peking University (Health Sciences), 2025, 57(5): 821-826.

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