{"ID":2859908,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04861","arxiv_id":"2510.04861","title":"A Clinical-grade Universal Foundation Model for Intraoperative Pathology","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.04861","url_pdf":"https://arxiv.org/pdf/2510.04861v2","authors":"[\"Zihan Zhao\",\"Fengtao Zhou\",\"Ronggang Li\",\"Bing Chu\",\"Xinke Zhang\",\"Xueyi Zheng\",\"Ke Zheng\",\"Xiaobo Wen\",\"Jiabo Ma\",\"Yihui Wang\",\"Jiewei Chen\",\"Chengyou Zheng\",\"Jiangyu Zhang\",\"Yongqin Wen\",\"Jiajia Meng\",\"Ziqi Zeng\",\"Xiaoqing Li\",\"Jing Li\",\"Dan Xie\",\"Yaping Ye\",\"Yu Wang\",\"Hao Chen\",\"Muyan Cai\"]","published":"2025-10-06T14:48:43Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}