S13: "A Humanoid General Medical Practitioner"
February 21, 2018 | 9:30-11:30 am
Description: How do doctors learn to diagnose? Can machines learn to do it too? First, the doctor uses a patient’s history and a physical exam to collect facts about the patient’s complaint or condition. Next, this information is collated to generate a comprehensive list of potential causes. Then questions and preliminary tests help eliminate one hypothesis and strengthen another - called “differential diagnosis.” Weight is given to how common a disease might be, and to the patient’s prior history, risks and exposures. The list narrows; the doctor refines the assessment. In the final phase, definitive lab tests, X-rays, or CT scans are deployed to confirm the hypothesis and seal the diagnosis. But could we do better? Humans aren’t set up to handle the vast and ever-growing chunks of health data the latest technologies are generating. No human mind can handle all that information, and we’re rapidly falling behind. In the past, incomplete information contributed to misdiagnosis and other errors. We relied on training, experience, and intuition to fill the gap. But increasingly, too much information is the challenge. Right now we have specialists for everything and none of them talk to each other as much as we’d like. On average, Medicare patients have seven major conditions. Wouldn’t it be better to have A.I. look at those conditions comprehensively - and one doctor, not seven, talk the results over with the patient? This new model and its future applications will flip healthcare on its head. Learn more about the emerging world of A.I. and its implications for the health industry.
Designed for: Anyone interested in the fascinating world of artificial intelligence
Facilitator: John Rittenhouse
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