用本体构建骨病诊断AI，提升可解释性与可靠性
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems
- 基于骨病本体设计分层神经网络，融合视觉语言模型
- 构建可解释的多模态诊断系统，整合影像与临床数据
- 适合医疗AI开发者和临床研究者参考，推动可信AI落地
医学人工智能系统常缺乏系统性领域知识整合，可能影响诊断可靠性。本研究提出一种基于本体的骨病诊断框架，与胡志明市骨科医院合作开发。该框架包含三项理论贡献：（1）基于骨病本体的分层神经网络架构，用于分割分类任务，通过提示词引入视觉语言模型；（2）增强本体的视觉问答系统，支持临床推理；（3）融合影像、临床与实验室数据的多模态深度学习模型，通过本体关系实现信息对齐。方法通过系统化知识数字化、标准化术语映射与模块化设计，保持临床可解释性。框架具备向其他疾病扩展的潜力，具有标准化结构和可复用组件。目前理论基础已建立，但因数据集与计算资源限制，实验验证尚未完成。未来工作将扩充临床数据集并开展全面系统验证。
原文摘要 · Abstract (English)
Medical artificial intelligence (AI) systems frequently lack systematic domain expertise integration, potentially compromising diagnostic reliability. This study presents an ontology-based framework for bone disease diagnosis, developed in collaboration with Ho Chi Minh City Hospital for Traumatology and Orthopedics. The framework introduces three theoretical contributions: (1) a hierarchical neural network architecture guided by bone disease ontology for segmentation-classification tasks, incorporating Visual Language Models (VLMs) through prompts, (2) an ontology-enhanced Visual Question Answering (VQA) system for clinical reasoning, and (3) a multimodal deep learning model that integrates imaging, clinical, and laboratory data through ontological relationships. The methodology maintains clinical interpretability through systematic knowledge digitization, standardized medical terminology mapping, and modular architecture design. The framework demonstrates potential for extension beyond bone diseases through its standardized structure and reusable components. While theoretical foundations are established, experimental validation remains pending due to current dataset and computational resource limitations. Future work will focus on expanding the clinical dataset and conducting comprehensive system validation.
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