Amsterdam and the Netherlands are already well on their way to being a true world-leader in AI and machine learning, and collaborations such as the one between University of Amsterdam (UvA) and the Netherlands Cancer Institute (NKI) are making sure AI applications developed here truly improve people's lives.

The new lab within the Innovation Center for Artificial Intelligence (ICAI) is a public-private collaboration commonly embraced in Amsterdam’s business, academic and public sectors - merging knowledge, networks and capital to bring research and innovations to society.

One of the potential healthcare applications being explored here is using AI to analyse images of tumours in order to prepare the best course of treatment for cancer patients. Jan-Jakob Sonke, one of the scientific directors of the lab, and ICAI Amsterdam director Marcel Worring share their expectations.

What do you want to achieve with the new lab?
Jan-Jakob Sonke: We want to use AI to improve cancer treatments.
Marcel Worring: The UvA has the AI expertise. The NKI has the necessary knowledge about oncology. Bringing that together will give this research an important impulse.
JJS: There are several topics to which AI can be applied. For example, understanding cancer at the cellular level. But also when deciding which therapy works best for a patient. And to find out what complaints a person can expect after treatment. Certainly in the beginning, however, the focus will be on image-driven therapy, which is my expertise. By analysing the image of a tumour, you can determine very precisely what should be irradiated or cut away, and what should not. AI can do that image analysis very accurately.

Pictured: ICAI Amsterdam director Marcel Worring

How can AI properly assess an image of tissue? 
MW: Before the advent of AI, we tried to encapsulate as much knowledge as possible in rules and had a computer program perform analysis on the basis of that knowledge. Over the past ten years, we have learned to let a system decide for itself what those rules are. You give a system many examples of images and tell it: this is cancer, this is not. AI can then independently find out what the characteristics are of an image with cancer. This method has proved to be much more successful.
JJS: AI can already identify pictures of dogs or cats. But scans of cancer patients are more difficult. The differences are more subtle – small details make the difference between cancer and no cancer.

Is AI already used in patient scans?
JJS: It happens in an experimental sense, but in practice it isn’t routine yet. The goal is to get our algorithms to such a level that they can actually be used for the treatment of the patient. I foresee that we will be able to put some developments into practice within five years.

Do you expect a great deal of knowledge exchange with other AI labs?
JJS: Certainly. Take self-driving cars. There, too, AI has to analyse images and make a decision: turn left or right? We want to do something similar for irradiation and operations. We want to continuously take live images of the tumour and use them to make adjustments during treatment, so that you radiate or operate in the right place.

How will the lab develop in the coming years?
MW: The combination of high-quality AI and oncology expertise makes this lab a unique place. The aim is that the lab will make even more cross-pollination possible in the future, also with other parties in Amsterdam. 

This article first appeared in an AI Special for New Scientist magazine.

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