heart-shaped pills coming out of a bottle

It’s incredibly important to understand how drugs interact with each other to keep patients out of dangerous and deadly situations. South Korea’s KAIST University recently shared that it’s developed a deep learning system to precisely predict interactions between certain drugs. With an accuracy rate of 92.4, this tech can predict 192,284 drug-to-drug interactions. But it doesn’t stop there. In its most recent iteration, it can predict drug-to-food interactions as well.

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Diabetes medical equipment

Using smartphone technology, a group of Harvard scientists are making it possible for people to monitor their blood potassium levels at home without drawing blood. The scientists developed a machine learning algorithm to detect potassium abnormalities in the blood. How does it work? They attach electrodes to a smartphone to record an ECG. Then, the machine learning algorithm flags subtle changes in the ECGs that correlated with blood potassium levels.

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When you get sick, receptors on your T-cells generally identify the antigen causing you problems, and then your T-cells trigger your immune-system response. Unfortunately, this may go awry with situations like cancer, where your body doesn’t recognize the growth as a problem.
However, some researchers don’t think we currently know enough about the T-cell receptors, so they built an AI application called ImmunoMap. It built a map of the T-cell receptor’s gene sequence so researchers could find receptors that are similar to each other—in the hopes that receptors with similar shapes may recognize similar antigens and trigger an immune response in the body.

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It can be tough for researchers to easily find new drugs to test for curing illnesses because government funding doesn’t cover the drug hunt. But Atomwise’s deep-learning tool searches through research and identifies possible new drug candidates for researchers to try, saving them precious time.

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