Artificial Intelligence in Healthcare – What We Know So Far

Artificial intelligence (AI) might not ever be able to develop the reassuring “bedside demeanor” that good doctors are renowned for, but it might be better able to precisely detect your illnesses.

An area of AI is called machine learning (ML). In ML, computers are “trained” to recognize possible dangers, such as dangerous cells shown in a scan, by being fed a massive amount of data and images.

It has been demonstrated that ML diagnosis is as accurate as that of skilled medical professionals, and it is already widely employed in the disciplines of pathology and cancer. In some experiments, it has even been demonstrated that ML can accurately identify diseases better than humans.

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Ali Hashemi, chairman, and co-founder of the Dubai-based GluCare Integrated Diabetes Center says there is numerous potential for artificial intelligence. “The trick is to put everything together in a way that makes sense and [provides] the care team and the doctor with insights they can use to take appropriate action. The goal is not AI. You have it in your toolbox. What counts is how you make use of these technologies to provide practical insights for medical professionals and patients.”

Diabetes is a metabolic condition that results in excessive blood sugar and is both prevalent and deadly. Diabetes-related high blood sugar levels that are untreated and persistent can harm the neurological system, eyes, and other organs, most frequently the kidneys. These days, patients are assisted in monitoring their blood sugar levels through wearable AI gadgets like wireless-enabled wearable monitors like the Fitbit.

One example of a continuous glucose monitoring (CGM) device that alerts the wearer in real-time when blood sugar levels are going to decrease or spike is the FreeStyle Libre 2 Flash Glucose Monitoring System, which is now provided to diabetics by the National Health Service (NHS) in the UK.

According to Hashemi, the variety of biometrics or digital biomarkers that wearable [devices] can acquire is always expanding. As a clinician, “such insights allow me superhuman abilities to have a remarkable impact on my patients.”

AI is also being utilized to assist diabetics to avoid going blind. Hashemi employs an AI-powered ophthalmoscope in his clinic to identify a condition known as diabetic retinopathy. Hashemi notes that diabetics, particularly those who have had their blood sugar under bad control for a while, may develop diabetic retinopathy, which is a deterioration of the retina and can result in blindness. “This device’s accuracy and precision are almost on par with those of hiring an ophthalmologist to perform the test. Its sensitivity is approximately 96 p “recent .”

Hashemi also makes the point that AI is frequently much more cost-effective than manual healthcare, in addition to helping with accurate disease diagnosis and detection. Therefore, its advancement might make access to medical care and treatment for more individuals, including those in underdeveloped nations, possible at a reduced cost.

One of the biggest obstacles to combining AI and healthcare is data governance. AI depends on private medical information, to which third parties still cannot have access in the majority of nations. Thoughts on the control of health data have changed as a result of the Covid-19 epidemic and the subsequent desire to stop its spread. Only 17% of the population in the post-pandemic world is now opposed to sharing personal medical information, according to a Wellcome Trust foundation survey in the UK.

“AI makes everyone more productive and helps us to actually leverage humans better,” says Hashemi, who is pleased with the move.

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