<scp>SegORG</scp> : Report Generation of Oral Potentially Malignant Disorders Image Based on Lesion Segmentation‐Enhanced <scp>LLM</scp>
By enhancing visual feature extraction and achieving efficient text alignment, SegORG offers an effective technical pathway for OPMDs reports automation. While single-center validation shows promise, multicenter trials are needed to assess generalizability.
Authors
- Yuqi Cao (ORCID: https://orcid.org/0000-0003-1822-7118)
- Qianming Chen (ORCID: https://orcid.org/0000-0002-5371-4432)
- Wei Chen (ORCID: https://orcid.org/0000-0001-5714-8282)
- Xiaohong Chen (ORCID: https://orcid.org/0000-0002-9797-8384)
- Rui Zhang (ORCID: https://orcid.org/0009-0007-4805-1279)
- Peng Huang (ORCID: https://orcid.org/0000-0002-4379-8871)
- Fudong Zhu (ORCID: https://orcid.org/0000-0002-9200-9541)
- Yaowu Chen (ORCID: https://orcid.org/0000-0001-7266-1535)
- Tingting Ding (ORCID: https://orcid.org/0000-0003-2836-0086)
- Xiang Tian (ORCID: https://orcid.org/0000-0003-0735-8454)
Institutions
- Wenzhou University (CN)
- Zhejiang University Medical College Affiliated Stomatological Hospital (CN)
- Stomatology Hospital (CN)
- State Key Laboratory of Industrial Control Technology (CN)
- Zhejiang University of Technology (CN)
Publication Details
- Journal
- Oral Diseases
- Published
- 2025-11-28
- DOI
- https://doi.org/10.1111/odi.70148
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.96