20 Jul New medical devices use Machine Learning to diagnose
Machine learning applied to medical devices
In the world of medical devices we are witnessing a phenomenon that describes “knowledge transfer”. It is the transition from a technology developed in a purely scientific research environment to its application in solving societal problems.
This process is behind many of the technological gadgets we enjoy today, such as cameras in mobile phones or trainers, and even some food products. Formula milk, for example, started out as a NASA development to feed astronauts.
This is Machine Learning. To explain how it works, the concept of Big Data must be defined. This is the data set of such a large volume and complex structure that its transformation into knowledge requires a specific analytical method.
One way to process Big Data is through devices that use an artificial intelligence component called Machine Learning. Machine Learning uses algorithms that train the computer to improve its performance at the end of a task. This occurs without any intervention by the programmer. Learning occurs automatically and through pattern or image recognition.
The application of Machine Learning to a medical device is where knowledge transfer takes place. Companies such as MMG are currently developing their medical device to diagnose different dental pathologies. Machine Learning is also being implemented in medical devices that recognise autism through brain imaging; or to predict complications from diabetes. There are also studies that have achieved early diagnosis of Alzheimer’s and other brain diseases, breast cancer metastases or skin cancer.
At Episkey Medical Consulting we are acquiring extensive experience in consulting and evaluation of medical devices using software based on Big Data analysis with Machine Learning and their uses as medical devices to aid the practice of medicine