IBM Collaborative to Bring Imaging into Daily Clinical Practice

The members of the imaging collaborative will use Watson to extract insights from unstructured imaging data and combine it with a broad variety of data from other sources, such as data from electronic health records (EHRs), radiology and pathology reports, lab results, doctors’ progress notes, medical journals, clinical care guidelines, and published outcomes studies.

Members of the collaborative will team with Watson Health cognitive computing experts to train Watson on cardiovascular disease (CVD), eye health, and other conditions using the data provided by the members of the collaborative or from population-based disease registries, which house millions of de-identified cases from around the world. Watson could then identify CVD and other conditions early and spot frequently overlooked heart conditions, such as congestive heart failure (CHF). IBM envisions Watson learning how patients' hearts are likely to start failing, and then monitoring the progression of the disease.

“With the ability to draw insights from massive volumes of integrated structured and unstructured data sources, cognitive computing could transform how clinicians diagnose, treat and monitor patients,” said Anne Le Grand, vice president of imaging at Watson Health. “Through IBM's medical imaging collaborative, Watson may create opportunities for clinicians to extract greater insights and value from imaging data while better managing costs.”

In addition to better preventive and personalized care, the collaborative could help health systems and companies save billions on inefficient and uncoordinated care. A recent National Academy of Medicine (Washington, DC, USA) study concluded that between 35% and half of the more than USD three trillion the United States spends on healthcare each year is wasted on suboptimal business processes and inefficient, inadequate, unnecessary, and uncoordinated care. By sharing and improving the use of imaging data, IBM hopes the collaborative will reduce waste and improve the quality of care.

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