Using automatic learning to advance image analysis

A unique strategy to solve this medical image-related problem is automatic learning. Automatic learning is a type of artificial intelligence that gives a computer the ability to learn from the data provided without being openly programmed. In other words: a machine is given different types of X-rays and MRI

Find the right patterns in them.
Then learn to notice those that are medically important.
The more data you provide the computer, the better your automatic learning algorithm is. Fortunately, in the health world there is no shortage of medical images. Its use can allow to put in the analysis of the image of the application to a general level. To better understand how automatic learning and image analysis are going to transform health care practices, let’s take a look at two examples.
Example 1:
Imagine that an individual goes to a trained radiologist with his medical images. That radiologist has never found a rare disease that the individual has. The chances of doctors diagnosing it correctly are minimal. Now, if the radiologist had access to automatic learning, the rare condition could easily be identified. The reason for this is that the image analysis algorithm could connect to images from around the world and then develop a program that detects the condition.
Example 2:
Another real-life application of AI-based image analysis is the measurement of the effect of chemotherapy. At this time, a medical professional has to compare the images of one patient with those of others to find out if the therapy has given positive results. This is a time consuming process. On the other hand, automatic learning can indicate in a matter of seconds if the treatment of cancer has been effective when calculating the size of the cancerous lesions. You can also compare patterns within them with those of a baseline and then provide results.
The day that medical imaging technology is as typical as Amazon that recommends what item to buy next based on your shopping history is not far. Their benefits not only save lives, they are also extremely economical. With the data of each patient that we add to the image analysis programs, the algorithm becomes faster and more accurate.

Not everything is attractive

It cannot be denied that the benefits of automatic learning in image analysis are numerous, but there are also some difficulties. Some obstacles that must be crossed before you can see widespread use are:

Employers who see a computer may not be understood by humans.
The process of selecting algorithms is at an incipient stage. It is still not clear what should be considered essential and what is not.
How safe is it to use a machine to diagnose?
Is it ethical to use automatic learning and there are legal ramifications of this?
What happens if the algorithm loses a tumor or incorrectly identifies a condition? Who do you think is responsible for the error?
Is it the duty of the physician to inform the patient of all abnormalities that the algorithm identified, even if no treatment is required for them?
You need to find a solution to all these questions before you can appropriate the technology in real life.