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The history of medical diagnosis is a march through painstaking observation. Ancient Egyptian physicians first diagnosed urinary tract infections by observing patterns in patients’ urine. To diagnose diseases of the heart and lungs, medieval doctors added core elements of the physical examination: pulse, palpation and percussion. The 20th century saw the addition of laboratory studies, and the 21st century of sophisticated imaging and genetics.
Despite advances, however, diagnosis has largely remained a human endeavor, with doctors relying on so-called illness scripts — clusters of signs, symptoms and diagnostic findings that are hallmarks of a disease. Medical students spend years memorizing such scripts, training themselves to, for example, identify the sub-millimeter variations in electrocardiogram wave measurements that might alert them to a heart attack.
But human beings, of course, err. Sometimes, misdiagnosis occurs because a doctor overlooks something — when the patterns of illness fit the script, but the script is misread. This happens in an estimated 15% to 20% of medical encounters. Other times, misdiagnosis occurs because the illness has features that do not match known patterns — they do not fit the script, such as when a heart attack occurs without telltale symptoms or EKG findings.
Artificial intelligence can help solve these two fundamental problems — if it’s given enough financial support and deployed correctly.