A Clinical-grade Universal Foundation Model for Intraoperative Pathology

• URL: http://arxiv.org/abs/2510.04861v2
• Date: Sun, 12 Oct 2025 15:33:15 GMT
• Title: A Clinical-grade Universal Foundation Model for Intraoperative Pathology
• Abstract summary: We introduce CRISP, a clinical-grade foundation model developed on over 100,000 frozen sections from eight medical centers.<n>CRISP was evaluated on more than 15,000 intraoperative slides across nearly 100 retrospective diagnostic tasks.<n>CRISP sustained high diagnostic accuracy under real-world conditions, directly informing surgical decisions in 92.6% of cases.
• Score: 18.600671472625866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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