Machine learning is omnipresent in every branch of our lives. The question that will be answered in this article is: How machine learning can impact our lives in terms of healthcare?
It’s obvious that using a processor, GPU and (probably soon) quant computers can help us detect cancer using images, assist in surgeries, give us advice based on our medical tests results and probably a lot more in the future. The world of AI is moving forward and the more data we use and analyze about healthcare the more we understand. Algorithms are improving, we are getting better and better. A few years ago it was hard to imagine self-driving cars. Right now we can see a technological shift which will change our lives. Autonomous vehicles are used right now on the streets. It’s true that some legal restrictions may appear, but we are evolving and it will change soon as technology grows and is tested better. The same situation is happening before our eyes - healthcare improvement. It is done using powerful machines which compute, think and give better results than humans. They are able to work 24 hours per day, 7 days per week, whole year with minimized downtime. Technology will replace human beings and we will be forced to change qualifications. It will not be the end of the world, though.
The technology helps us detect and cure diseases faster. Based on our medical results, genes and previous data analysis we will be able to improve our lives. Right now it’s shown that machine learning cancer detectors work better than experienced doctors. What will be achieved after that? The use of Artificial Intelligence in medicare will grow and in some situations outperform human beings. The algorithms will find health problems on the image - if any occurs, then the doctor will be notified and he or she will decide what to do. Machine learning can also show some abnormalities and deviations. It can propose drugs and it’s dosage. More autonomous systems will be created, maintained and constantly improved.
Based on that we can assume a few things about machine learning in healthcare:
- lowers human error
- detect diseases from blood, liquids, medicare images, x-rays, timestamped data gathered during a day (for example using a simple smartwatch)
- can assist humans
- discover new drugs
- find a right treatment
- automatically detect dangerous situation
The last thing to mention here is that AI will lead us to a better retirement. We can wear a smartwatch, have some cameras around the house or even both! For example with an AI the system is able to see if someone is lying on the floor or is in any dangerous situation. From a smartwatch we know some basic information like heart rate and blood pressure. These information is basic, but can save our lives. Such a system can notify an emergency directly or some other people - friends, family. They can check if everything is fine with the person. Such systems can save our lives.
Going deeper into technology we have a lot of methods to accomplish such tasks. Starting from fast cloud data processing, through algorithms, machine learning, neural networks, deep learning and ending with distributed, federated learning or agent systems. Right now we can build machine learning systems for all of the tasks mentioned in this article using these methods.
Data processing is always beneficial and needed. We can process streams and tones of bytes with current auto-scaling cloud systems in a few seconds. Anyway it’s a place to optimize our resources. This will have an impact on the next steps.
Deep machine learning can be used to perform any predictions, discovery, tasks and lower human error. It will analyse data and based on previous decisions the model will make its own ones. They can be validated and used.
There are plenty of solutions waiting for your needs. A great example of solving a problem with private data sharing is federated learning. Which trains a local model for each participant and then those models can be concatenated into a global model. Imagine a situation where we have a few big medicare companies and all of them willing to cooperate on a shared model which will detect cancer, but do not want to share the data itself. Using federated learning we can allow each of the companies to train their own model and later concatenate it into one global model which can be used by all of them. Sharing data without actually sharing it was never so simple!
All of those above lead to better healthcare.
As individuals we will be able to profit from better healthcare services.