A clinically-oriented foundation model for intraoperative pathology

Why clinicians should know about it

• Picked for Pathology and Forensic Medicine (paper of the day, 16 September 2026).

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.

Abstract as published, via PubMed.