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

arXiv: https://arxiv.org/abs/2506.04756 · HTML: https://mlsanity.com/paper/2506.04756/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax](https://mlsanity.com/paper/2303.01615/) (2023)
- [Combining Machine Learning and Ontology: A Systematic Literature Review](https://mlsanity.com/paper/2401.07744/) (2024)
- [Recent Advances in Medical Image Classification](https://mlsanity.com/paper/2506.04129/) (2025)
- [A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks](https://mlsanity.com/paper/2506.04121/) (2025)

## Abstract (arXiv)

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.