In a recent Nature Medicine article, MSK’s Dr. Thomas Fuchs and his colleagues describe how they trained artificial intelligence (AI) to process pathology slides at large scale. The research was completed with the start-up Paige.AI and is reported by The Cancer Letter, Tech Crunch, Becker’s Healthcare, and 360 DX.
To train deep learning systems, researchers typically need to manually curate data, a time-consuming process that has previously prevented large-scale AI applications to pathology datasets. In this study, researchers instead trained the system using only the slide-level diagnostic information already in the electronic health record. The study included 44,732 slides of prostate, skin, or axillary lymph node tissue from 15,187 patients at 800 institutions. Their calculations indicate that the model could reduce a pathologist’s workload by up to 75%, classifying cancer without sacrificing sensitivity.
To learn more about artificial intelligence, including a list of the latest AI-related publications from MSK authors, check out the Library’s new LibGuide.