Artificial Intelligence for Health

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In this subspecialisation, you will learn to understand and develop AI-based solutions for real-world problems in healthcare.

Artificial intelligence (AI) is permeating our day-to-day life. There is a high chance that you are already using AI to translate between languages, recognise objects in photos, or navigate to your destination. Globally, there are high expectations of what AI could mean for healthcare. For example, AI could assist clinicians in day-to-day decision-making, monitor patients after complex surgery, or make healthcare more accessible in developing countries.

Yet, with all this enthusiasm comes some healthy skepticism. Indeed, AI is often perceived as a black box, and when applied to healthcare, given its high-stakes nature, there are obvious ethical concerns about the fairness and explainability of AI methods. In the AI4Health subspecialisation, you will take a peek inside the black box and see how mathematics works at the core of this technology. You will gain a solid foundation in statistics, machine learning, and operations research, and learn how to leverage the potential of AI for high-stakes real-world applications in healthcare.

AI from theory to practice

One unique aspect of this subspecialisation is that you will work on three or more healthcare-related case studies. These real-world case studies have been developed in collaboration with clinical partners in hospitals and demonstrate the versatility of AI applications in healthcare. You will put your theoretical understanding of the mathematics behind AI into practice. We challenge you to dive into domain-specific conditions and cross boundaries to disciplines like computer science and medicine.

Some examples of problems you will learn how to solve
  • The COVID-19 crisis has shown how vulnerable the logistics of our healthcare system are. There is a prominent role that smart scheduling algorithms can play to more effectively use the resources available in hospitals.
  • Clinical specialists like radiologists and pathologists look at hundreds of medical images to detect a needle in the haystack. AI-based algorithms could significantly improve image formation and analysis for diagnosis and prognosis.
  • AI algorithms are good at providing a prediction for some input, but this prediction is often binary. To allow interpretation by humans and adoption of AI in healthcare applications, mathematically quantifying the uncertainty in an automated prediction is critical.
  • More and more people are wearing watches and other wearables equipped for heart rate monitoring or electrocardiograms. These devices collect enormous amounts of data which AI techniques could analyse to improve the management of patients with heart diseases.

Career opportunities

While there is a lot of buzz around AI, the big question is how AI can be safely and effectively used for healthcare applications. Answering this question requires experts who understand the mathematics of AI, consequently see its strengths and weaknesses for applications, and can communicate this with users. In this subspecialisation, you will get an in-depth knowledge of this versatile field. Our teaching staff collaborates with clinical partners in The Netherlands and abroad and companies in healthcare such as Siemens and Philips. This track will give you a solid foundation to contribute to this rapidly developing field. AI experts with a mathematical background are in high demand in academic hospitals and the strongly growing healthcare industry.

Continue as a researcher: obtain a PhD

Instead of pursuing a professional career as a mathematical model expert right away after obtaining your master’s, you can opt to follow a PhD programme at Twente Graduate School (TGS). This involves spending four years studying a particular research area in-depth at one of our research institutes. Obtaining your PhD will earn you the title of Doctor (Dr).

courses and admission

This subspecialisation comprises 2 years of study in which you will need to amass a total of 120 EC. Please be aware that you may be required to follow a Pre-Master's first, depending on your bachelor's degree. 

Theoretical courses are focused on creating a solid foundation in statistics, machine learning and operations research. Furthermore, a big part of the study programme is putting theory into practice and working on real life case studies, developed in close collaboration with clinical partners in hospitals. More information on courses, graduation and research follows soon.

Jan Schut
Coordinator Master's programme Applied Mathematics
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