Evaluating cell AI foundation models in kidney pathology with human-in-the-loop enrichment
This study establishes a benchmark for the development and deployment of cell AI foundation models tailored to real-world data. The proposed framework, which leverages foundation models with reduced expert annotation, supports more efficient workflows in clinical pathology.
Authors
- Can Cui (ORCID: https://orcid.org/0000-0002-2159-5387)
- Agnes B. Fogo (ORCID: https://orcid.org/0000-0003-3698-8527)
- Ruining Deng (ORCID: https://orcid.org/0000-0001-6300-8518)
- Catie Chang (ORCID: https://orcid.org/0000-0003-1541-9579)
- Junlin Guo (ORCID: https://orcid.org/0000-0001-9300-0314)
- Tianyuan Yao (ORCID: https://orcid.org/0000-0002-1848-079X)
- Yuankai Huo (ORCID: https://orcid.org/0000-0002-2096-8065)
- Siqi Lu
- Shilin Zhao (ORCID: https://orcid.org/0000-0002-3921-3965)
- Yuan Lin (ORCID: https://orcid.org/0000-0003-4150-7759)
- Mengmeng Yin
- Mitchell Wilkes
- Juming Xiong
- Z H Tao
- Haichun Yang
- Marilyn Lionts (ORCID: https://orcid.org/0000-0002-9889-3390)
- Quan Liu (ORCID: https://orcid.org/0000-0002-4956-5737)
- Yu Wang (ORCID: https://orcid.org/0000-0003-2420-3585)
Institutions
- Vanderbilt University (US)
- Shanghai Ninth People's Hospital (CN)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1038/s43856-025-01205-x
- Citations
- 2
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 1.61