AI in the medical field

AI is definitely a new technology. Especially now that people like Google and Amazon have bought their AI bots so you can ask what the weather is, what’s on your calendar or remind you to walk the dog. However, there are far more serious uses for AI, and the medical field is leading the technology march.

Imagine you had a leading surgeon, you want them to teach as many up and coming surgeons as possible. AI makes this possible as the skills of top surgeons can be programmed into an AI program that can be used for training purposes. How about practicing these learned skills? Again, AI combined with Virtual Reality will allow a training student to practice operating in real-time, with AI feedback suggestions as well as running scenarios of good and bad.

However, AI also helps with the more mundane areas of health care. From simple situations like managing appointments to much more complex support environments such as research information, AI supports, improves and helps the medical field.

So how does AI improve what can be on the face of reasonably simple solutions? To start, we need to explore the power of AI.

In its simplest terms, AI is defined as software that thinks and makes decisions in a similar way to the human brain. When you consider that the human brain doesn’t even understand how it works, it can be a brave definition. Also, when you consider that AI has been around and used for at least 20 years, but it is only in the last few years that it has started to be very useful, it becomes a challenging definition. Despite what many science fiction books and movies say, AI is not set to take over the world, but rather become a support environment.

So we come to the definition that AI can work the same way as the human brain, responding to situations and producing lives as scenarios and answers. If you also think about Siri and Alexa, it can provide realistic answers to a large number of questions answered in different ways. But anyone who has despaired of getting Siri to answer the question you have actually asked, there are still limitations.

So what’s in the future for medical use of AI? It is good to first clarify that there are companies such as John Snow Labs, the winner of the 2018 AI solution provider that is at the forefront of AI research and that the future is rapidly advancing and coming closer.

Bringing life-changing medicines to the market has always been a long and costly process. AI can not only support the processes involved, but also help work its way through the analysis produced, make life-like, human-like decisions to shorten searches and decisions. Now, of course, there must be a final human decision, but the decision path is shorter.

So how does machine learning become so useful?

At the most basic machine learning are skilled at running millions of algorithms in a short timeframe and providing the resulting conclusions to the human operator for their review and decision. The beautiful thing is that this speed of test algorithms is much faster than the human brain can perform.

The other key difference to normal powerful computing software is that AI or machine learning software can use these algorithms to learn from the patterns and then create its own logic. In medical research, these algorithms are tested many millions of times until uniform results are produced. These results are then transferred to the medical professional to make the human decision based on AI research.

When looking at areas like medical research where there are thousands of different possible outcomes and even more variables combined with a healthy link of things that can go wrong, it’s easy to see why machine learning programs are so welcome by the medical field.

When looking at medical treatment, there are countless factors that can be wrong where machine learning comes to the forefront. Often combined with Virtual Reality (VR), realistic surgeries can be created so that the surgeon can practice their skills without fear of harm or even killing the patient. The surgeon can practice cardiac transplantation several times with AI, providing multiple scenarios based on the surgeon’s activities until they are safe enough to perform the surgery on a real live person.

Using similar scenarios, treatment studies can be tried and tested until an appropriate new treatment is found, where AI suggests different methods, results and problems that surgeons are working on.

For new surgical techniques, AI is really emerging, testing thousands, if not millions, of different scenarios and outcomes with even more problems that may arise, all safely in a black box and away from the patient.

And it is with patient safety that AI comes at the forefront of medical research and treatment.