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Can AI help a health system under pressure?

27 OCTOBER 2022

Can AI help a health system under pressure?

In Denmark we have a shortage of medical specialists and nurses. In 2025, according to the Danish Nurses’ Organization, there will be a shortage of at least 6,000 nurses, and the lack of medical specialists is also a huge problem for a hospital sector under a historically high pressure.


How do we become better at using artificial intelligence in healthcare?

17 OCTOBER 2022

How do we become better at using artificial intelligence in healthcare?

There is an increasing demand in Denmark for new and more advanced healthcare services. In the coming years, there will be more elderly people with treatment needs and a decreasing youth population to take care of the elderly. The challenges call for us to think differently, so that we can jointly develop a well-functioning healthcare system that can provide the best treatment methods.

The use of artificial intelligence is an important part of the solution when resources need to be optimized and we need to think differently. But is our healthcare system ready to implement the new solutions, and what challenges will arise in the meeting between digital research and everyday life in a busy hospital?

“Artificial intelligence and machine learning can improve the ways we prevent and diagnose diseases, optimize treatments, increase quality and reduce errors. A huge number of technological innovations are emerging right now, many of which are promising research-based AI solutions, and yet it is a challenge to get them tested and implemented in the healthcare sector, says Thomas Riisgaard Hansen, director of Digital Research Centre Denmark (DIREC). 

What is holding the development back and what are the actual challenges? Is it that technology is getting closer, but still too limited and full of errors to create actual value in the healthcare sector? Is it that data and legislation complicate the development of algorithms? Is it that the healthcare system has problems incorporating new technology and changing work processes? Is it a lack of resources and money? Or does the problem lie elsewhere? This hot topic was discussed in the session ‘How to navigate the challenges of implementing groundbreaking AI in the healthcare sector’ at this year’s Digital Tech Summit. 

“It is a major task to use the technological opportunities in the healthcare system and it also requires us not to be deceived by dazzling promises about what the technology can do but, instead, we must work purposefully to exploit the actual opportunities and to remove or reduce the barriers that interfere,” says Thomas Riisgaard Hansen, who has worked with health innovation for 20 years and moderated the panel discussion. 

He was accompanied by technology companies, researchers, innovators, and health professionals, who gave their own take on how we can jointly support the development and implementation of new solutions that will benefit patients and staff.

The session presented three concrete cases about implementation of AI in the Danish healthcare system:  

Getting Access to Health Data and Ways to Leverage it in the Health Sector
Henrik Løvig, Enversion & Gitte Kjeldsen, Danish Life Science Cluster

Getting AI innovations implemented internationally
Mads Jarner Brevadt, Co-founder & CEO, Radiobotics & Janus Uhd Nybing, Ledende Forskningsradiograf, Bispebjerg og Frederiksberg Hospital samt Medstifter, Radiologisk AI Testcenter RAIT

Getting Research Implemented in the Daily Practices in a Hospital Setting
Mads Nielsen, Professor, KU andIlse Vejborg, Head of Department, Rigshospitalet

Each case is based on experiences with the implementation of artificial intelligence in the healthcare system and highlighted the challenges and best practices that have been identified from the perspective of the technology developers and not least of the healthcare professionals.

The session was organized by DIREC, Pioneer Centre for AI, CBS, DTU, and Danish Life Science Cluster. 





Explainable AI to increase hospitals’ use of AI

26 November 2021

Explainable AI to increase hospitals' use of AI

In a new DIREC project, AI researchers are collaborating with hospitals to create more useful AI and AI algorithms that are easier to understand.

AI (artificial intelligence) is gradually gaining ground in assistive medical technologies such as image-based diagnosis, where artificial intelligence analyzes CT scans with superhuman precision. AI, on the other hand, is rarely designed as a collaborator for healthcare professionels.

In a new human-AI project EXPLAIN-ME – supported by the national research center DIREC, AI researchers together with medical staff will develop explanatory artificial intelligence (Explainable AI – XAI) that can give clinicians feedback when training in hospitals training clinics.

“In the Western world, about one in ten diagnoses is judged to be incorrect, so patients do not get the right treatment. The explanation may be due to a lack of experience and training. Our XAI model will help the medical staff make decisions and act a bit like a mentor who gives advice and response when they train,” explains Professor at DTU Compute and Project Manager Aasa Feragen.

In the project, DTU, the University of Copenhagen, Aalborg University, and Roskilde University collaborate with doctors at the training and simulation center CAMES at Rigshospitalet, NordSim at Aalborg University Hospital, and oncologists at the Department of Urology at Zealand University Hospital in Roskilde.

Ultrasound scan of pregnant women

At CAMES, DTU and the University of Copenhagen will develop an XAI model that looks over the shoulder of doctors and midwives when they ultrasound scan ‘pregnant’ training dolls at the training clinic.

In the field of ultrasound scanning, clinicians work on the basis of specific ‘standard plans’, which show different parts of the fetus’ anatomy to make it easier to see and react in case of complications. The rules are implemented in the XAI model, which is integrated into a simulator that gives the doctor ongoing feedback.

“It would be great if XAI could help less trained doctors to do scans that are on a par with the highly trained doctors.”
Professor and Projekt Manager Aasa Feragen

The researchers train the artificial intelligence on real data from Rigshospitalet’s ultrasound scans from 2009 to 2018, and it is primarily images from the common nuchal scan and malformation scans that are offered to all Danish pregnant women approximately 12 and 20 weeks into the pregnancy. When the XAI models will be ready to use at the training clinic, first they have to check whether the model also works in the simulator, since the EAI model is trained on real data, while the training doll is artificial data.

According to doctors, the quality of ultrasound scans and the ability to make accurate diagnoses depends on how much training the doctors have received.

“If our model can tell the doctor during the scan that a foot is missing in the picture, the doctor may be able to learn faster. If we get the XAI model to tell us that the probe on the ultrasound device needs to be moved a bit to get everything in the picture, then maybe it can be used in medical practice as well. It would be great if XAI could help less trained doctors to do scans that are on a par with the highly trained doctors,” says Aasa Feragen.

Research associate professor and head of CAMES’ research team for artificial intelligence Martin Grønnebæk Tolsgaard emphasizes that many doctors are interested in getting help from AI technology to find the best treatment for patients. Here is explainable AI the way to go.

“Many of the AI models that exist today do not provide very good insight into why they come to a particular decision. It is important for us to become wiser on that. If the model does not explain why it comes to a given decision, then clinicians do not believe in the decision. So if you want to use AI to make clinicians better, then we need good explanations, like Explainable AI.”

Ongoing feedback on robotic surgery

Robotic surgery allows surgeons to perform their work with more precision and control than traditional surgical tools. It reduces errors and increases efficiency, and the expectation is that AI will be able to improve the results further.

In Aalborg, the researchers will develop an XAI model that supports the doctors in the training center NordSim, where both Danish and foreign doctors can train surgery and operations in simulators on e.g. pig hearts. The model must provide ongoing feedback to the clinicians while they are training an operation without interfering, says Mikael B. Skov, professor at Department of Computer Science at Aalborg University:

“Today, it is typically the case that you only get to know if you should have done something different when you have finished training an operation. We would like to look at how you can come up with this feedback more continuously to better understand whether we have done something right or wrong. The feedback should be done in such a way that the people learn faster and, at the same time, make fewer mistakes before they have to go out and do real operations. We, therefore, need to look at how to develop different types of feedback, such as warnings without interrupting too much”.

Image analysis in kidney cancer

Doctors often have to make decisions under time pressure, e.g. in connection with cancer diagnoses to prevent cancer from spreading. A false-positive diagnosis, therefore, could cause a healthy kidney removed and other complications to be inflicted. Although experience shows that AI methods are more accurate in assessments, clinicians need a good explanation of why the mathematical models classify a tumor as benign or malignant.

In the DIREC project, researchers from Roskilde University will develop methods in which artificial intelligence analyzes medical images for use in diagnosing kidney cancer. Clinicians will help them understand what feedback is needed from the AI models to balance what is technically possible and what is clinically necessary.

“It is important that the technology can be included in the hospitals’ practice, and therefore we focus in particular on designing these methods within ‘Explainable AI’ in direct collaboration with the doctors who actually use it in their decision-making. Here we draw in particular on our expertise in Participatory Design, which is a systematic approach to achieve the best synergy between what the AI researchers come up with in terms of technological innovations and what doctors need,” says Henning Christiansen, professor in computer science at the Department of People and Technology at Roskilde University.

About DIREC – Digital Research Centre Denmark

The purpose of the national research centre DIREC is to bring Denmark at the forefront of the latest digital technologies through world-class digital research. To meet the great demand for highly educated IT specialists, DIREC also works to expand the capacity within both research and education of computer scientists. The centre has a total budget of DKK 275 million and is supported by the Innovation Fund Denmark with DKK 100 million. The partnership consists of a unique collaboration across the computer science departments at Denmark’s eight universities and the Alexandra Institute.

The activities in DIREC are based on societal needs, where research is continuously translated into value-creating solutions in collaboration with the business community and the public sector. The projects operate across industries with focus on artificial intelligence, Internet of Things, algorithms and cybersecurity among others.



Partners in the project EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

  • DTU (DTU Compute – Department of Mathematics and Computer Science)
    University of Copenhagen
  • Aalborg University
  • Roskilde University
  • CAMES – Copenhagen Academy for Medical Education and Simulation at Rigshospitalet in Copenhagen
  • NordSim – Center for skills training and simulation at Aalborg University Hospital
  • Department of Urology at Zealand University Hospital in Roskilde

Project period: 1 October 2021 to 30 April 2025

Aasa Feragen
DTU Compute
M: +45 26 22 04 98

Anders Nymark Christensen
DTU Compute
+45 45 25 52 58


Redefining healthcare – a conversation with Managing Director of DIREC, Thomas Riisgaard Hansen


A conversation with Managing Director of DIREC, Thomas Riisgaard Hansen

CEO and President of Falck, Jakob Riis shares his conversation with Managing Director of DIREC (Digital Research Centre Denmark), Thomas Riisgaard Hansen. Thomas considers digitalisation an imperative for health as a mean not only for optimisation but also to create better outcomes.