One of the most potent weapons available to doctors and patients when fighting disease is time. When it comes to conditions like cancer, stroke, or diabetic retinopathy, the earlier they are diagnosed, the better the chances for successful treatment.
New diagnostic tools incorporating artificial intelligence are currently being developed that may help doctors in different specialties make more accurate diagnoses more quickly. The artificial intelligence researchers program a computer with an algorithm that mimics the neural networks of the brain, enabling it to learn to recognize key features when exposed to a large number of diagnostic imaging sources, such as dermoscopy, images taken with a retinal camera, or CT scans of the brain. In some tests across more than one specialty, the artificial intelligence was as good or better at identifying abnormalities than specialists analyzing the same images.
As many as 50 percent of patients with diabetes do not see their eye doctor on a yearly basis, which leaves them vulnerable to development and exacerbation of diabetic retinopathy. An important part of managing care of retinopathy is early detection, and if not treated, the condition can cause blindness. Yet patients who do not see an ophthalmologist on a regular basis are not properly screened.
Now, however, even physicians who do not specialize in eye care can screen patients for diabetic retinopathy with the aid of artificial intelligence software recently approved by the Food and Drug Administration called the IDx-DR device. Analyzing images taken with a retinal camera, the software issues either a negative or positive result for “more than mild diabetic retinopathy.” If the result is negative, a rescreening in 12 months’ time is recommended, but if the result is positive, the doctor is advised to refer the patient to an eye care professional as soon as possible.
A recent study in Germany indicated that artificial intelligence could be a useful tool to help dermatologists focus on and address the most suspicious lesions on a patient’s skin. In the study, expert-level physicians looked at dermoscopic images of either benign moles or malignant melanomas while the same images were fed into the computer. With little to no information about the patient, the dermatologists correctly diagnosed melanomas approximately 87 percent of the time, and when provided with patient data, their accuracy improved to 89 percent. In both cases, however, the artificial intelligence software outshone them with a correct melanoma detection rate of 95 percent.
A 2017 study in the Netherlands found that, much like in the dermatology study, artificial intelligence outperformed pathologists in detecting breast cancer that had spread to the lymph nodes, specifically micrometastases, or small clusters of tumor cells, which are notoriously easy for pathologists to miss on routine examination.
During every minute that a patient is having a stroke, an estimated 1.9 million neurons die, according to Dr. David Freeman, a neurologist at the Mayo Clinic who is developing an artificial intelligence system that he hopes will cut down on the time it takes to diagnose a particular kind of stroke called an intracerebral hemorrhage. Dr. Freeman hopes that his AI system could cut down the time it takes for doctors to analyze CT images and decide on the proper treatment by up to 30 minutes, decreasing damage to the brain during the event and hopefully saving more lives.
Building an intelligent brain inside a computer may sound like the stuff of science fiction, and physicians may wonder what role they will play in a world in which artificial intelligence can diagnose disease. Researchers developing these technologies insist that artificial intelligence will never take the place of doctors, meaning it’s critical for organizations to create leadership teams that will keep pace with this life-saving technology.
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