The number of AI products in healthcare, particularly in the field of medical imaging, is rapidly growing. There are over 200 products available for the radiology market, and platforms are emerging that lower the barrier for software developers to make their solutions widely available. However, to date, few of these AI products are being used in daily practice, despite many companies striving to gain a foothold in hospitals and radiology departments. On the other hand, management boards and radiologists are struggling with identifying products that can improve healthcare, reduce workload, or cut costs. Solid studies validating AI products in radiology are scarce. Most studies have been funded by the industry, measuring the performance of a single algorithm, while ignoring integration into clinical workflows, time and cost savings, and cost-effectiveness analysis.
The ‘AI for Radiology’ platform, developed by Kicky van Leeuwen in her PhD project over the past few years, enables end-users to access an overview of the available products in the radiology market and provides transparency about their performance. Additionally, we have developed a framework, Project AIR, which allows us to conduct independent multi-vendor, multi-center comparisons of similar products.
We are seeking an ambitious PhD student to work on this three-year project, which should result in a PhD thesis. The goal of this PhD project is to bring more transparency in the field of commercially available AI solutions in healthcare. You will use our developed Project AIR framework to validate and compare more commercially AI products to provide insights into the performance of the products.
Another important part of the project is to develop and coordinate a national course aimed at healthcare professionals on AI for healthcare, part of the ICAI Academy. The course will explain how AI works, what the role of AI in healthcare is, what products are currently available, and what to keep in mind when evaluating and implementing AI products in healthcare. The course will consist of an online part and full-day workshops that will be held four times per year.
Furthermore, you will have the opportunity to explore the AI market beyond the field of radiology and expand the overview of commercially available products to other areas in healthcare.
The Diagnostic Image Analysis Group is at the forefront of developments in AI for healthcare, with various in-house developed products already translated or in the process of being translated to the clinic. This project is a collaboration between clinical researchers of the Department of Medical Imaging of the Radboud University Medical Center, the Diagnostic Image Analysis Group, the Innovation Center for Artificial Intelligence and the start-up Health AI Register (the company started by Kicky van Leeuwen that pursues the continuation of AIforRadiology.com). You will also collaborate with other hospitals in our Dutch AI network, and the AI vendors to achieve the above goals.
- MSc degree (or equivalent) in (Technical) Medicine
- Strong interest in Artificial Intelligence in Healthcare
- Excellent communication skills in English and the ability and willingness to work in an interdisciplinary research team
- Interest in teaching and educating healthcare professionals on the role of AI in healthcare
Radboud University Nijmegen Medical Center
Nijmegen is the oldest Dutch city with a rich history and one of the liveliest city centers in the Netherlands. Radboud University has over 17,000 students. Radboud University Medical Center is a leading academic center for medical science, education, and health care with over 8,500 staff and 3,000 students.
Terms of employment for PhD students
You will be appointed as a researcher at the Radboud University Medical Center, Department of Medical Imaging, with the standard salary and secondary conditions for clinical PhD students in the Netherlands. Your appointment is for three years. Your work is expected to lead to the thesis.
To apply for this PhD vacancy, click here. Applications will be processed immediately upon receipt and the positions will be filled as soon as a suitable candidate has been found. The hiring process will be closed on December 10, 2023. For further information, please contact Maarten de Rooij or Steven Schalekamp.