Paige Announces World’s First Clinical-Grade AI in Pathology
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Paige Announces World’s First Clinical-Grade Artificial Intelligence in Pathology

Paige Announces World’s First Clinical-Grade AI in Pathology

Article Published in Nature Medicine Provides Further Scientific Evidence for Deployment of Computational Decision Support Systems to Improve Patient Care

PRESS RELEASE — NEW YORK — Paige, the leader in computational pathology focused on building artificial intelligence (AI) to transform the clinical diagnosis and treatment of cancer, today announced the publication of an article in Nature Medicine, a leading monthly journal publishing original peer-reviewed research in all areas of medicine, describing an AI system for computational pathology that achieves clinical-grade accuracy levels. The paper provides further scientific evidence that pathologists’ work in diagnosing and treating cancer can be complemented and aided through the deployment of computational decision-support systems to improve patient care.

The team of scientists responsible for the work described in the article developed specially-designed deep learning algorithms to build a system that can detect prostate cancer, skin cancer and breast cancer with near-perfect accuracy. These algorithms are based on a vast dataset of nearly 45,000 de-identified, digitized slide images from more than 15,000 cancer patients from 44 countries.

“After years of in-depth, comprehensive modeling, training, and testing, we are thrilled that Nature Medicine has published our paper, which demonstrates our ability to train accurate classification models at unprecedented scale, and validates our mission to create the world’s first clinical-grade, artificial intelligence in pathology,” said Dr. Thomas Fuchs, Co-Founder and Chief Scientific Officer of Paige, who led the work at his lab at Memorial Sloan Kettering Cancer Center (MSK).

The paper outlines how a series of novel algorithms created using datasets ten times larger than those that have been manually curated performed better and also are more generalizable. The significance of this new development hinges on the fact that curating datasets can be prohibitively expensive and time-intensive. By eliminating the need to curate datasets, Paige can now develop many more highly accurate algorithms that can be built into clinical decision support products to help pathologists around the world drive better patient care.

“The publication in Nature Medicine of the algorithm developed by Dr. Fuchs’ lab is an important milestone for Paige. It demonstrates that AI has the potential to support pathologists in delivering quantitative and more accurate diagnoses, improving treatment for patients worldwide. Leveraging even larger training sets, over the past year, Paige has created novel vendor-agnostic systems that demonstrate even better accuracy,” said Dr. Christopher Kanan, lead AI scientist at Paige.

Paige plans to commercialize several of these solutions to address the most pressing needs in pathology to improve patient care. Paige has already built on the academic work described in Nature Medicine to develop a clinical product, based on the technology currently under review by the U.S. Food and Drug Administration as a designated Breakthrough Device, for an intended indication different than the one described in the article.

All data collection, research, and analysis for this research were conducted exclusively at MSK in New York City, led by Dr. Fuchs and his student Gabriele Campanella. The publication of the study’s findings was the result of a collaboration between numerous researchers and clinicians and made possible by Paige’s partnership with MSK. All data were de-identified and did not contain any protected health information or label text. The full article, published in Nature Medicine on July 15, 2019, and titled “Clinical-grade Computational Pathology using Weakly Supervised Deep Learning on Whole Slide Images,” can be found online at

About Paige

Based in New York City, Paige is bringing together the world’s leading experts in machine learning, computational pathology, and clinical practice who are committed to fundamentally improving the diagnosis and treatment of cancer.  For more information, please visit

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