# Bridging the Skill Gap in Clinical CBCT Interpretation with CBCTRepD

arXiv: https://arxiv.org/abs/2603.10933 · HTML: https://mlsanity.com/paper/2603.10933/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [KiUT: Knowledge-injected U-Transformer for Radiology Report Generation](https://mlsanity.com/paper/2306.11345/) (2023)
- [Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation](https://mlsanity.com/paper/2303.17579/) (2023)
- [A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images](https://mlsanity.com/paper/2010.10563/) (2020)
- [Dia-LLaMA: Towards Large Language Model-driven CT Report Generation](https://mlsanity.com/paper/2403.16386/) (2024)

## Built on by (most cited first, 1 of 1)

- [DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning](https://mlsanity.com/paper/2608.18878/) (2026)

## Abstract (arXiv)

Generative AI has advanced rapidly in medical report generation; however, its application to oral and maxillofacial CBCT reporting remains limited, largely because of the scarcity of high-quality paired CBCT-report data and the intrinsic complexity of volumetric CBCT interpretation. To address this, we introduce CBCTRepD, a bilingual oral and maxillofacial CBCT report-generation system designed for integration into routine radiologist-AI co-authoring workflows. We curated a large-scale, high-quality paired CBCT-report dataset comprising approximately 7,408 studies, covering 55 oral disease entities across diverse acquisition settings, and used it to develop the system. We further established a clinically grounded, multi-level evaluation framework that assesses both direct AI-generated drafts and radiologist-edited collaboration reports using automatic metrics together with radiologist- and clinician-centered evaluation. Using this framework, we show that CBCTRepD achieves superior report-generation performance and produces drafts with writing quality and standardization comparable to those of intermediate radiologists. More importantly, in radiologist-AI collaboration, CBCTRepD provides consistent and clinically meaningful benefits across experience levels: it helps novice radiologists improve toward intermediate-level reporting, enables intermediate radiologists to approach senior-level performance, and even assists senior radiologists by reducing omission-related errors, including clinically important missed lesions. By improving report structure, reducing omissions, and promoting attention to co-existing lesions across anatomical regions, CBCTRepD shows strong and reliable potential as a practical assistant for real-world CBCT reporting across multi-level care settings.