Dr Watson goes back to school

  • 16 November 2012
Dr Watson goes back to school
A classic depiction of Sherlock Holmes' Dr Watson

Medical students in Cleveland will use IBM’s Watson to analyse challenging medical cases as the company looks to advance the supercomputer’s use in medical training.

The IBM team of researchers that created Watson will work with Cleveland Clinic clinicians, faculty and medical students to enhance the capabilities of Watson’s deep question answering technology for the area of medicine.

When considering different patient cases, medical students come up with hypotheses then connect evidence in reference materials and the latest journals to identify diagnoses and treatment options.

“This process of considering multiple medical factors and discovering and evidencing solution paths in large volumes of data reflects the core capabilities of the Watson technology,” an IBM statement says.

“A collaborative learning and training tool utilizing the Watson technology will be available to medical students to assist in their education to learn the process of navigating the latest content, suggesting and considering a variety of hypotheses and finding key evidence to support potential answers, diagnoses and possible treatment options.”

Cleveland Clinic’s chief information officer Dr Martin Harris said the “global medical library” is forever expanding as more and more information is added by physicians and scientists around the world.

“Technology like this can allow us to leverage that medical library to help train our students and also find new ways to address the public health challenges we face today," he said.

Students will help improve Watson’s language and domain analysis capabilities by judging the evidence it provides and analysing its answers.

Over time, the expectation is that Watson will get “smarter” about medical language and how to assemble good chains of evidence from available content

The collaboration will also focus on using Watson to process an electronic medical record based on a deep semantic understanding of the content within an EMR.

 

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