What it is
CRISP is a foundation model for intraoperative (frozen-section) pathology, developed on over 100,000 frozen sections from ten medical centers. It was evaluated on more than 15,000 intraoperative slides across nearly 100 retrospective diagnostic tasks and generalized 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, it directly informed surgical decisions in 92.6% of cases, and human-AI collaboration reduced diagnostic workload by 35%, avoided 105 ancillary tests, and detected micrometastases with 87.5% accuracy.
Why it matters
Computational pathology has advanced, but a lack of large-scale prospective validation has kept it out of routine surgical workflows. Here the model was tested prospectively on over 3,000 patients under real-world conditions and directly informed surgical decisions in 92.6% of cases, moving beyond retrospective benchmarks toward the point of care where intraoperative pathology actually operates.
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Filed underAI in cancer detection, Surgical Simulation and Training, Radiomics and Machine Learning in Medical Imaging