AI-assisted health scans

The demand for screening tests, which allow for the dynamic monitoring of health, is actively growing, while the number of doctors remains about the same. Statistics show that more than 97% of patients screened have no potential health problems, but the doctor still has to devote considerable time to them.
The introduction of artificial intelligence for computer analysis of medical images as well as for diagnosing pathologies enables doctors to diagnose more quickly and more accurately, which in turn enables them to serve more patients, increase recovery rates and reduce the cost of examinations.

Using machine learning technologies, artificial intelligence is being trained to analyze images and identify abnormalities. Modern systems can already analyze eye and skin images, MRIs and X-rays, CT scans, and mammograms.

A medical technology company, Pr3vent, is already in active use in the US. Its essence is an eye screening system based on artificial intelligence to diagnose ophthalmological pathologies in healthy preterm children. Through the use of Pr3vent, doctors can diagnose eye diseases more quickly and accurately, enabling early treatment to be initiated and preventing vision loss. Potentially, such a campaign can examine up to 4 million children per year, saving society up to 3 billion dollars.

Medical professionals are also already actively introducing AI-based tools that can be used in elderly health screening for the early diagnosis of dementia. The performance of these tools is assessed by quantifying the volume of a patient’s brain tissue. Presumably, earlier detection of neurodegenerative brain diseases will help to more effectively curb the progression of pathological processes and improve future prognosis.

Artificial intelligence is poised to revolutionize medical practice by increasing the efficiency and accuracy of health screening in image-based fields. AI algorithms learn when scientists “feed” them many hundreds or thousands of images (such as mammograms).

It is expected that very soon AI will be able to successfully analyze scanned CT images of patients with a suspected stroke, flagging those with a particularly high chance of brain hemorrhage and sending them to a separate folder for review by a radiologist. AI will also be able to help identify tumors that the radiologist is at risk of missing.

The prospects for AI in human health scans are very promising.



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