11May
deepmind medical research
By: Hp Creative Space On: May 11, 2017

Deeplearning AI

The problem with new terminology is that it takes non-medical people too long to understand what is being said. Deeplearning AI is simply using new methods to identify diseases.

It uses special radiomic procedures to help it apply precision medicine to individual medical cases. Radiomics is, of course, a simple image taking by radiologists. Precision medicine is a medical specialty that seeks to eliminate variables and other inconsistent data from the identification of diseases.

Why Precision Medicine?

One of the main reasons why precision medicine is becoming the accepted medical strategy is because it is non-invasive. Doctors worry about invading the body too many times in order to make any diagnosis.

An invasive procedure can be costly, time-consuming and may not produce the results hoped for. Precision medicine relies, then, on what is called biomarkers. These biomarkers provide more specific information without having the doctor open up a patient.

Precision medicine looks at different factors. For example, a patient’s environment supplies some clues as to the identity of a possible medical problem. Clinical information does not qualify as a biomarker for many reasons.

Often clinical information is inaccurate, it is missing pertinent information and may have missed a slowly developing disease. Because of these inaccuracies, a new medical strategy had to be developed.

Deeplearning AI tries to find new, more accurate and exacting medical information gathering systems. The ultimate goal is to find the clues precisely and quickly so medical intervention can help save the patient’s life.

Radiomics and Precision Medicine

One major reason medical professionals are turning to radiomics is because doctors routinely prescribe medical imaging to peer inside the body. By examining these images, doctors can discover medical information that rivals the information gathered by more invasive techniques.

These techniques include a biopsy, microscopy and even some DNA retrieval and examination. The non-invasive procedure is what is attractive here. By opting for these new methods, doctors still can gather the same amount of information that the traditional methods discover.

Some Weaknesses of Deeplearning AI

The advantages of Deeplearning AI and precision medicine are often undermined by the weaknesses that are inherent in new medical techniques. One of the main weaknesses is the limitations imposed upon the procedure because the radiomic expert’s knowledge does not extend further than it does. In other words, while the expert knows a lot, he does not know enough.

Another weakness in Deeplearning AI is the process is highly time-consuming. Traditional information gathering procedures take less time to get the same information. A final weakness is that each step of the process brings more problems and ends up complicating the radiomic analysis.

Some Final Words

Deeplearning AI and precision medicine may be the next step in medical diagnosis. But both systems have far too many problems to overcome before they can be truly effective. The motivation behind these strategies, patient care, is sound.

Doctors do need better ways to treat their patients. The problem is they need practical strategies that do not take too much time.

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