Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.10933v1</id>\n    <title>Bridging the Skill Gap in Clinical CBCT Interpretation with CBCTRepD</title>\n    <updated>2026-03-11T16:18:07Z</updated>\n    <link href='https://arxiv.org/abs/2603.10933v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.10933v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n    <published>2026-03-11T16:18:07Z</published>\n    <arxiv:primary_category term='cs.CV'/>\n    <author>\n      <name>Qinxin Wu</name>\n    </author>\n    <author>\n      <name>Fucheng Niu</name>\n    </author>\n    <author>\n      <name>Hengchuan Zhu</name>\n    </author>\n    <author>\n      <name>Yifan Sun</name>\n    </author>\n    <author>\n      <name>Ye Shen</name>\n    </author>\n    <author>\n      <name>Xu Li</name>\n    </author>\n    <author>\n      <name>Han Wu</name>\n    </author>\n    <author>\n      <name>Leqi Liu</name>\n    </author>\n    <author>\n      <name>Zhiwen Pan</name>\n    </author>\n    <author>\n      <name>Zuozhu Liu</name>\n    </author>\n    <author>\n      <name>Fudong Zhu</name>\n    </author>\n    <author>\n      <name>Bin Feng</name>\n    </author>\n  </entry>"
}