The gap in our ability to systematically evaluate how clinical decision support affects how clinicians think and behave hinders the design and implementation of safe and effective AI for patient care. BMIR researchers aim to create a proof of concept of a method for generating a comprehensive, low-bias feature pool of downstream clinical decisions made in the real world setting that can be potentially generalized to become a new way to study the effects of AI on how clinicians make decisions. https://hai.stanford.edu/grants/

BMIR'S Andrew Gentles and Olivier Gevaert, and Aaron Newman from the Department of Biomedical Data Science, contributed greatly to the completion of The Cancer Genome Atlas (TCGA) PanCan initiative, a decade-long process that culiminated in the release of a series of 27 papers.

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Using health records, Stanford researchers developed an algorithm for scoring the risk of a stroke patient experiencing a heart condition known as atrial fibrillation, a major risk factor for a second stroke.

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The computational immunologist Purvesh Khatri embraces messy data as a way to capture the messiness of disease. As a result, he’s making elusive genomic discoveries

To distill a clear message from growing piles of unruly genomics data, researchers often turn to meta analysis a tried and true statistical procedure for combining data from multiple studies.

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What do a Stanford Assistant Professor and Monte Vista High School student have in common? Disrupting the current state of the art in cancer diagnosis and treatment!

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Analyzing other people's data has been linkened to research parasitism. Purvesh Khatri is "repurposing data to ask and answer questions that are not addressable using traditional approaches". He is "a proud data parasite."

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