Large language model-based multimodal system for detecting and grading ocular surface diseases from smartphone images
MOSAIC exhibited exceptional few-shot learning capabilities, achieving high accuracy in OSD management with minimal training examples. This system has significant potential for smartphone integration to enhance the accessibility and effectiveness of OSD detection and grading in resource-limited settings.
Authors
- Wenfang Wang (ORCID: https://orcid.org/0000-0003-1603-6166)
- Yangyang Wang (ORCID: https://orcid.org/0009-0009-8549-5930)
- Pengfei Zhang (ORCID: https://orcid.org/0000-0002-1017-929X)
- Gang Chen (ORCID: https://orcid.org/0000-0002-5145-5529)
- Xiu Li (ORCID: https://orcid.org/0000-0003-0403-1923)
- Zhongwen Li (ORCID: https://orcid.org/0000-0001-7187-5566)
- Wei Chen (ORCID: https://orcid.org/0000-0003-2462-7652)
- Weihua Yang
- Zhouqian Wang
Institutions
- Sichuan University (CN)
- Wenzhou Medical University (CN)
- Affiliated Eye Hospital of Wenzhou Medical College (CN)
- West China Second University Hospital of Sichuan University (CN)
- Southern Medical University Shenzhen Hospital (CN)
Publication Details
- Journal
- Frontiers in Cell and Developmental Biology
- Published
- 2025-05-23
- DOI
- https://doi.org/10.3389/fcell.2025.1600202
- Citations
- 10
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 13.60
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Ningbo Municipality
- Natural Science Foundation of Zhejiang Province