AI in the medical field
TechnologyAI is definitely an emerging technology. Especially now that companies like Google and Amazon have bought their AI bots that let you 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 leads the technology market.
Imagine you have a leading surgeon, you would want him to teach 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 student in training to practice operating in real time, with AI feedback suggestions and execution of good and bad scenarios.
However, AI is also helping in the more mundane areas of the health service. From simple situations like appointment management to much more complex support environments like research information, AI is supporting, improving and helping the medical field.
So how does AI improve such reasonably simple solutions? To begin 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 might be a brave definition. When you also consider that AI has been around and used for at least 20 years, but only in the last few years has it started to become 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, but to become a supportive environment.
So we come to the definition that AI can function in the same way as the human brain, reacting to situations and producing life-like scenarios and responses. Also, if you think about the likes of Siri and Alexa, you can produce realistic answers to a large number of questions that are answered in a variety of ways. However, for anyone who’s been desperate to get Siri to answer the question she’s actually 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 winning AI solutions provider of 2018, who are at the forefront of AI research and that future is progressing rapidly and ever closer.
Bringing life-changing medicines 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 decisions similar to those of human life 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 run 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 the human brain can perform.
The second big difference from normal powerful data processing software is that AI 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 produced. These results are then delivered to the medical professional for human decision making 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 healthy set of things that can go wrong, it’s easy to see why machine learning programs are so welcome in 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 combined with virtual reality (VR), realistic operations can be set up, allowing the surgeon to practice his or her skills without fear of injuring or even killing the patient. The surgeon can practice the heart transplant multiple times with the AI providing 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 is found, with the AI suggesting different methods, outcomes and problems as surgeons work.
For new surgical techniques, the AI really shines, testing thousands if not millions of different scenarios and outcomes with even more issues to come, all safely inside 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.