AI in the medical field
AI is definitely an emerging technology. Especially now that companies like Google and Amazon have bought their artificial intelligence bots that allow you to ask what the weather is like, what’s on your calendar, or remind you to walk the dog. However, there are much more serious uses for AI and the medical field is leading the way in technology.
Imagine you have a leading surgeon, you would like to be taught as many emerging surgeons as possible. AI makes this possible, as the skills of the best surgeon can be programmed into an AI program that can be used for training purposes. How about practicing these learned skills? Once again, AI combined with virtual reality will allow a trainee to practice running in real time, with AI suggestions, as well as good and bad execution scenarios.
However, AI is also helping in the more mundane areas of the healthcare service. From simple situations like managing appointments to much more complex supportive environments like research information, AI is supporting, enhancing and assisting the medical field.
So how does AI improve such, what could be, at first glance, reasonably simple solutions? To start with, we need to investigate the power of AI.
In its simplest terms, AI is defined as software that thinks and makes decisions similar to the human brain. When you consider that the human brain doesn’t even understand how it works, at first glance it could be a bold definition. When you also consider that AI has been around and used for at least 20 years, but only in recent years has it started to be very useful, it becomes a challenging definition. Despite what many science fiction books and movies claim, AI is not meant to take over the world – it will become a supportive environment.
So we come to the definition that AI can function in the same way as the human brain, react to situations and produce real scenarios and responses. Also, if you think of people like Siri and Alexa, you can produce realistic answers to a large number of questions that are answered in various ways. However, anyone desperate to get Siri to answer the question they really asked, there are still limitations.
So what’s in the future for medical uses of AI? Well, to clarify first, there are companies like John Snow Labs, the 2018 winning AI solutions provider, who are at the forefront of AI research and the future is fast moving forward and fast approaching.
Bringing life-changing drugs to market has always been a long and expensive process. AI can not only support the processes involved, but also help work through the analysis produced, making life-like and human-like decisions to shorten searches and decisions. Now obviously there must be a final human decision, but the decision paths are shorter.
So how is machine learning becoming so useful?
In its most basic form, machine learning has the ability to execute millions of algorithms in a short period of time and provide the resulting conclusions to the human operator for review and decision. The beauty is that this algorithm testing speed is much faster than what the human brain can perform.
The second big difference from powerful normal data processing software is that artificial intelligence or machine learning software can use these algorithms to learn from patterns and then create their own logic. Within medical research, these algorithms are tested many millions of times until consistent results are obtained. These results are then delivered to the medical professional for a human decision based on AI research.
When you look at areas like medical research, where there are thousands of different and even more variable possible outcomes, combined with a handful of things that can go wrong, it’s easy to see why machine learning programs are so well received by the medical field. .
When looking at medical treatment, it is the myriad of factors that can go wrong where machine learning comes to the fore. Often times, realistic operations combined with Virtual Reality (VR) can be set up, allowing the surgeon to practice their skills without fear of injuring or even killing the patient. The surgeon can practice heart transplantation numerous times with AI that provides multiple scenarios based on the surgeon’s activities until he is confident enough to perform the operation on a real person.
Using similar scenarios, treatment research can be tried and tested until a suitable new treatment has been found, and the AI suggests different methods, results, and issues as surgeons work.
For newer surgical techniques, AI really comes to the fore, testing thousands, if not millions, of different scenarios and outcomes with even more problems that can arise, all safely within a black box and away from the patient.
And it is with patient safety that AI comes to the fore in medical research and treatment.