Health

Innovative Treatments the Medical World Needs Right Now

Gene editing to treat and prevent diseases CRISPR gene editing allows researchers to change DNA, including repairing mistakes. Although it’s a relatively new technology, it has… Aisha Abdullah - December 13, 2022
Source: Pixabay

Gene editing to treat and prevent diseases

CRISPR gene editing allows researchers to change DNA, including repairing mistakes. Although it’s a relatively new technology, it has already had a substantial impact on medicine and earned Nobel Prize in Chemistry its inventors. The technology has been tested as a treatment for rare blood disorders, cancers, heart disease, and Alzheimer’s disease, with many more on the horizon. But CRISPR’s potential isn’t limited to its ability to treat diseases; it may also be able to prevent them from ever developing. It may also be used to prevent inherited diseases as well as certain cancers, neurodegenerative diseases, or immune disorders that are caused to single gene mutation.

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Virtual reality that diagnoses and treats disease

From video games and augmented reality apps to career training and socializing, virtual reality has caught on in a big way. Unsurprisingly, the tech has also made an impact in the healthcare field with exciting tools like virtual reality surgical studios that let surgeons visualize every aspect of a procedure before the patient is even in front of them. One of the next frontiers of virtual reality is likely to be the diagnosis and treatment of mental illnesses. Studies are already underway that explore virtual reality as a tool to detect post-traumatic stress syndrome (PTSD), attention deficit/hyperactivity disorder (ADHD), and anxiety.

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Machine learning models that predict disease risk

Imagine getting a personalized, artificial-intelligence-driven prediction of diseases that you are most at risk of developing. You could be empowered to make lifestyle changes or begin preventative care to decrease your risks. Augmented artificial intelligence models are a type of machine learning designed to pull data from multiple sources to assist in humans making more accurate decisions. A model that uses input from a dozen electrocardiograms (ECGs) — tests that measure electrical activity in the heart— to predict heart issues and an artificial neural network (a simplified model of the human brain) that can accurately predict chronic illnesses are two recent examples of ambitious uses of machine learning to determine disease risk.

Where Do We Find this Stuff? Here Are Our Sources:

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