The number of AI products for radiology is rapidly growing. There are over 100 companies active in this market and platforms are appearing that lower the barrier for software developers to make their solution widely available. Until now, very few of the AI products are being used in daily practice, although many of the developers are trying to get a foothold in hospitals and radiology departments. On the other side, management boards and radiologists are struggling to find out which products may improve healthcare, reduce workload or reduce costs. Solid studies to validate AI products in radiology are scarce. Most studies were performed with funding from the industry and only measure the performance of a single algorithm and ignore integration in clinical workflow, time- and cost-savings and cost-effectiveness analysis.
The goal of this project is to develop a generic validation strategy that includes technical efficacy (e.g. diagnostic performance and robustness to differences in input), diagnostic efficacy (impact on radiological practice, patient outcomes), ease-of-use for implementation in routine care, cost-effectiveness (differentiating gains in accuracy and gains in efficiency, e.g., reduced reading times), and efficacy at the overall healthcare and societal level.
The Diagnostic Image Analysis Group is at the forefront of these developments and various in-house developed products have been or are being translated to the clinic. In a collaboration of the Department of Radiology & Nuclear Medicine of the Radboud University Medical Center and the Department of Radiology of Jeroen Bosch Hospital, we have access to a wide spectrum of data, pathology, and knowledge, which is important for the quality of our test and validation results.
We are looking for an ambitious PhD student to work on this three-year project. The work should result in a PhD thesis. This project is a collaboration between clinical researchers of the Department of Radiology & Nuclear Medicine of the Radboud University Medical Center and Jeroen Bosch Hospital and the Diagnostic Image Analysis Group of the Department of Radiology & Nuclear Medicine of the Radboud University Medical Center Nijmegen.
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 Nijmegen Medical Centre (RUNMC) is a leading academic center for medical science, education and health care with over 8,500 staff and 3,000 students.
You will be appointed as a clinical PhD student at the Radboud University Medical Center Nijmegen, Department of Radiology & Nuclear Medicine, with the standard salary and secondary conditions for clinical PhD students in the Netherlands. You will work within the Radboud Institute for Health Sciences of the Radboud University Nijmegen Medical Center. Your performance will be evaluated after 1 year and will normally be extended by 2 years.
For further information please contact Maarten de Rooij (Maarten.deRooij@radboudumc.nl) or Steven Schalekamp (Steven.Schalekamp@radboudumc.nl).
A link to apply will be posted here soon. You can already express your interest by e-mail. The following should be included: CV, list of followed courses and grades, a letter of motivation, and reprints or, preferably, links to your Master thesis or any scientific publications or reports that you have written. Applications will be processed immediately and the position will be open until a suitable candidate has been hired.