Mayo Clinic Will Use Watson To Match Cancer Patients With Clinical Trials

Watson Goes Live at Cleveland Clinic

IBM and the Mayo Clinic today announced a partnership that will bring the Watson supercomputer to Mayo’s oncology department, where it will be used to expedite the process of matching patients with relevant clinical trials. The announcement came during Mayo Clinic’s annual Transform symposium, which is being held this week in Rochester, NY.

The task of evaluating a patient’s record and then matching the patient with the best clinical trial for them is currently a time intensive, manual process, but for patients with cancer, inclusion in a promising trial is often looked to as a last chance for recovery. The trials provide patients with an otherwise bleak prognosis access to truly cutting edge in healthcare research. Mayo Clinic researchers are actively engaged in 8,000 human clinical trials at any given time, and over 170,000 trials are being run globally at any given time. Each trial is has specific inclusion criteria, and researchers hope Watson will help expedite the process of finding appropriate trials for sick patients.

Watson is already loaded with patient selection criteria for some 170,000 clinical trials seeking patients across the globe. Next, Watson will be trained by clinicians and computer scientists to learn the steps involved in analyzing a patient record and matching them with relevant clinical trials. Over time, the algorithms used to match patients will be refined and Watson will become more efficient at its task. Researchers at Mayo hope that Watson is able to expedite the process of identifying participants for clinical trials so that research can move forward faster and more patients can get access to treatments with the highest probability of helping them.

Researchers are also hoping that Watson will be able to help locate far-away patients for inclusion in clinical trials focused on rare diseases. Mayo’s researchers explain that many important clinical trials are never completed because there were not enough patients available to participate in the study.


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