Medical research is published with tremendous speed, making it nearly impossible for a doctor to keep up. Artificial Intelligence could be the answer. The growing amounts of available data enables the use of artificial intelligence in health care, as well as the increasingly sophisticated machine learning algorithms. Yet relatively little of these methods are used in health care.

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Artificial Intelligence in HealthCare
Esther Weusthof - Data Scientist Mobiquity Europe
The rise of ‘the internet of things' leads to many new possibilities. Existing processes
can be optimized and improved. Also in health care ‘the Internet of things’ can be of
great value. Some examples are the presence of sensors that generate tons of data.
One could think of measuring the blood pressure, medication and the heart rate. Not
only the increasing amounts of data available enables the use of artificial intelligence
in health care, also the increasingly sophisticated machine learning algorithms make a
great contribution.
For decades, doctors examined tissue samples manually for breast cancer. A few
years ago a computer system based on a database of tissue data was developed, this
computer system was able to diagnose breast cancer itself. This computer system
turns out to be so precise, that it better diagnoses than the doctors and pathologists.
Medical research is published with tremendous speed making it nearly impossible for a
doctor to keep up, which is not the case for a computer. Artificial Intelligence offers
many other benefits for health care, such as lower costs, and better results. Yet
relatively little of these methods are used in health care, which could be explained by
the fact that artificial intelligence in healthcare is still in its infancy.
Another way in which artificial intelligence can make a major contribution, is to better
tailor a treatment plan or the medication to a specific patient. Nowadays the treatment
of a patient is mostly determined based on the results of medical examination to large
groups of people, the so-called randomized controlled trials. But many patient groups
are never involved in these trials, such as children or pregnant women. Therefore,
startup companies have emerged that focus on big data. From thousands of
documented disease cases, they want get useful information that can help the doctor
in giving the best advice for a particular patient. One may wonder whether this
eventually means the end of the GP. This will most likely not be the case. A GP is so
much more than just a decision maker. The doctor sees how it is going with a patient.
Sometimes a bladder infection is simply solved with medication, but often there is
more to it. The doctor can trace a potentially serious problem, such as inflammation of
a kidney, with a small physical investigation. Such examination is extremely difficult to
replace with a machine.
Altogether, I expect machine learning can have a great contribution in health care, in
particular for specific applications. It will take some time before both fields are
completely combined en working together smoothly, but there is undoubtedly a lot to
gain!