By offering a second perspective, data driven machine learning algorithms can offer critical secondary feedback and aid in early detection. For example, researchers in China have begun using a machine learning algorithm that predicts the likelihood of a patient waking from a coma. In several instances, the algorithm correctly predicted that patients would awake when doctors on staff did not. While undoubtedly there were times when the algorithm was wrong and the doctors were right, it is nonetheless accurate enough to provide invaluable second opinions for clinical diagnoses.
Healthcare image data is especially valuable for machine learning. As just one example, when evaluated by ophthalmologists, a deep learning algorithm had over 90 percent specificity and sensitivity for diabetic retinopathy when fed thousands of retinal photographs. In dermatology, when fed 130,000 patient images, an algorithm was comparably accurate to dermatologists in identifying skin cancer. While it’s undoubtedly helpful to provide an accurate second opinion that isn’t subject to human error, these algorithms are providing a service that doctors already provide: diagnostics. This begs the question: are there any areas where machine learning provides aid that doctors can not?
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