AI in medical imaging has the potential to break new ground in disease diagnosis as well as patient preparation and monitoring.
FREMONT, CA: Researchers are discovering new ways to improve diagnosis and recovery planning as medical imaging advances. The application of artificial intelligence to medical imaging is one of the most promising fields of research currently underway. Artificial intelligence (AI) is the ability of software or machines to mimic human cognitive thought. As a result, they can assist in problem-solving activities. AI in medical imaging has the potential to break new ground in disease diagnosis as well as patient preparation and monitoring. Here are a few examples of AI in medical imaging:
Identifying Slices of Interest: Since each slice is just a few millimeters long, a single CT or MRI scan of a patient will produce hundreds of images. Going through an individual slice to look for anomalies will take a long time for the radiologist. AI will sift through all of the slices and choose only the important ones to the radiologist.
Retrieving Old Records: AI can go through databases to fetch older images from patients’ health records. These photos can be used to compare to any recent images. This can be used to track disease development or to measure drug efficacy.
Detecting Finer Abnormalities: Minimal color or contrast variations can be undetectable to the naked eye. On the other hand, these variations may indicate the onset of invasive disease at an early stage. AI can detect even minute variations, assisting in diagnostic precision that is impossible to achieve by manual means.
Large Scale Screening: A new application of AI in medical imaging is large-scale medical screening. A newly developed intelligence-based framework for screening medical images through multiple hospital databases has been developed. The AI was programmed to recognize large vessel obstruction, which is a precursor to a stroke. If all goes well, the app will send a priority message to the patient and the stroke specialist. It would shorten the time between diagnosis and care, potentially improving patient outcomes.
Preparing Diagnostic Reports: AI will be able to convert color and contrast abnormalities into real diagnostic findings. This could be accomplished by feeding data from previous case reports. AI can also be used to produce imaging reports based on diagnostic data.
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