Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems

TL;DR

This paper introduces an ontology-based framework for bone disease diagnosis that enhances AI interpretability and reliability by integrating domain knowledge into neural networks, VQA, and multimodal models.

Contribution

It presents a novel ontology-guided neural network architecture, a VQA system, and a multimodal model for bone disease diagnosis, emphasizing systematic knowledge digitization and interpretability.

Findings

Framework demonstrates potential for clinical interpretability.

Design supports extension to other medical domains.

Experimental validation is planned for future work.

Abstract

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.