In this MEDICA-tradefair.com interview, Dr. Markus Wenzel defines the term machine learning, explains how far AI solutions have come in today’s medical practice and ventures a guess as to how they might evolve.
Dr. Wenzel, what do the terms machine learning and deep learning actually mean?
Dr. Markus Wenzel: The term machine learning suggests that a computer sets its own goals and learns something. In my view, that is not quite the case. It actually refers to the process in which a researcher feeds data into the computer aimed at teaching it something. That’s why I believe machine teaching would be a better term for this process.
Irrespective of the term, machine learning is seen as the path to artificial intelligence or AI in short. However, this is only true to a limited extent. In this case, you do not search for methods to replicate and artificially create intelligence. Instead, you look for ways to teach computers to solve a cognitive task as quickly and efficiently as possible and do at least as well as a human being. The method used for this purpose does not necessarily have to be deep learning. It can be any other way of drawing conclusions from data, albeit deep learning is a highly efficient method.
What tasks can AI take over for physicians and perhaps even solve better than they can?
Wenzel: Generally speaking, this refers to duties and responsibilities where physicians systematically have weaknesses because the tasks are repetitive and humans tend to quickly become tired. Or it can be tasks that do not take full advantage of the knowledge and skills of a medical specialist. During a typical day, doctors look at many findings and test results. A radiologist, for instance, reviews many images with a specific task in mind, albeit most images generally only show normal results. These images are recorded based on a suspected diagnosis, though an entire stack of images often only shows results on a handful of images.
This type of time-consuming search task is one of the cognitive abilities where computers can learn and excel in, meaning they review large volumes of data, locate possible areas of concern and presort for the physician so that he or she no longer has to look through all the images.