A clinically-oriented 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 clinically oriented foundation model developed on over 100,000 frozen sections from ten medical centers, specifically designed to provide Clinically-oriented 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 6 institutions, 14 tumor types, and 24 anatomical sites-including previously unseen sites and rare cancers. In a prospective cohort of over 3,000 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 suggest that CRISP represents a clinically oriented approach to AI-driven intraoperative pathology, with the potential to support surgical decision-making and facilitate the translation of computational methods into clinical practice.