A Clinical-grade Universal Foundation Model for Intraoperative Pathology
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| author | Zhao, Zihan Zhou, Fengtao Li, Ronggang Chu, Bing Zhang, Xinke Zheng, Xueyi Zheng, Ke Wen, Xiaobo Ma, Jiabo Wang, Yihui Chen, Jiewei Zheng, Chengyou Zhang, Jiangyu Wen, Yongqin Meng, Jiajia Zeng, Ziqi Li, Xiaoqing Li, Jing Xie, Dan Ye, Yaping Wang, Yu Chen, Hao Cai, Muyan |
| author_facet | Zhao, Zihan Zhou, Fengtao Li, Ronggang Chu, Bing Zhang, Xinke Zheng, Xueyi Zheng, Ke Wen, Xiaobo Ma, Jiabo Wang, Yihui Chen, Jiewei Zheng, Chengyou Zhang, Jiangyu Wen, Yongqin Meng, Jiajia Zeng, Ziqi Li, Xiaoqing Li, Jing Xie, Dan Ye, Yaping Wang, Yu Chen, Hao Cai, Muyan |
| contents | Intraoperative pathology is pivotal to precision surgery, yet its clinical impact is constrained by diagnostic complexity and the limited availability of high-quality frozen-section data. While computational pathology has made significant strides, the lack of large-scale, prospective validation has impeded its routine adoption in surgical workflows. Here, we introduce CRISP, a clinical-grade foundation model developed on over 100,000 frozen sections from eight medical centers, specifically designed to provide Clinical-grade Robust Intraoperative Support for Pathology (CRISP). CRISP was comprehensively evaluated on more than 15,000 intraoperative slides across nearly 100 retrospective diagnostic tasks, including benign-malignant discrimination, key intraoperative decision-making, and pan-cancer detection, etc. The model demonstrated robust generalization across diverse institutions, tumor types, and anatomical sites-including previously unseen sites and rare cancers. In a prospective cohort of over 2,000 patients, CRISP sustained high diagnostic accuracy under real-world conditions, directly informing surgical decisions in 92.6% of cases. Human-AI collaboration further reduced diagnostic workload by 35%, avoided 105 ancillary tests and enhanced detection of micrometastases with 87.5% accuracy. Together, these findings position CRISP as a clinical-grade paradigm for AI-driven intraoperative pathology, bridging computational advances with surgical precision and accelerating the translation of artificial intelligence into routine clinical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Clinical-grade Universal Foundation Model for Intraoperative Pathology Zhao, Zihan Zhou, Fengtao Li, Ronggang Chu, Bing Zhang, Xinke Zheng, Xueyi Zheng, Ke Wen, Xiaobo Ma, Jiabo Wang, Yihui Chen, Jiewei Zheng, Chengyou Zhang, Jiangyu Wen, Yongqin Meng, Jiajia Zeng, Ziqi Li, Xiaoqing Li, Jing Xie, Dan Ye, Yaping Wang, Yu Chen, Hao Cai, Muyan Machine Learning Intraoperative pathology is pivotal to precision surgery, yet its clinical impact is constrained by diagnostic complexity and the limited availability of high-quality frozen-section data. While computational pathology has made significant strides, the lack of large-scale, prospective validation has impeded its routine adoption in surgical workflows. Here, we introduce CRISP, a clinical-grade foundation model developed on over 100,000 frozen sections from eight medical centers, specifically designed to provide Clinical-grade Robust Intraoperative Support for Pathology (CRISP). CRISP was comprehensively evaluated on more than 15,000 intraoperative slides across nearly 100 retrospective diagnostic tasks, including benign-malignant discrimination, key intraoperative decision-making, and pan-cancer detection, etc. The model demonstrated robust generalization across diverse institutions, tumor types, and anatomical sites-including previously unseen sites and rare cancers. In a prospective cohort of over 2,000 patients, CRISP sustained high diagnostic accuracy under real-world conditions, directly informing surgical decisions in 92.6% of cases. Human-AI collaboration further reduced diagnostic workload by 35%, avoided 105 ancillary tests and enhanced detection of micrometastases with 87.5% accuracy. Together, these findings position CRISP as a clinical-grade paradigm for AI-driven intraoperative pathology, bridging computational advances with surgical precision and accelerating the translation of artificial intelligence into routine clinical practice. |
| title | A Clinical-grade Universal Foundation Model for Intraoperative Pathology |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.04861 |