I am a Technical Medicine and Health Sciences graduate of the University of Twente. Since August 2020, I have been working as a PhD candidate as part of the AMICUS project. AMICUS is short for Artificial Intelligence in Medical Imaging for Cancer User Support and the main research focus is to leverage artificial intelligence technologies to improve care for cancer patients. Through Health Technology Assessment, new interventions can be modelled to evaluate the health and economic impact on patients, healthcare institutions and society. The focus of this reseach is to facilitate the clinical adoption of artificial intelligence in medical imaging in daily practice.


  • Medicine and Dentistry

    • Breast Cancer
    • Patient
    • Mammography
    • Diagnosis
    • Health
    • Inpatient
    • Intelligence
  • Nursing and Health Professions

    • Health Economics


Within the healthcare sector, Artificial Intelligence (AI) has seen a substantial rise in development over the past years due to growing interest and its potential impact on healthcare delivery and effectiveness. Innovations supporting the digitization of health data have led to new opportunities and challenges in healthcare delivery. Challenges originate from the realization that the growth in digital health data is quickly exceeding the human capacity to process and analyze it in routine clinical practice. Specifically in medical image analysis, the increasing amount of imaging data generated by a range of modalities is becoming a bottleneck for diagnosis, therapy-planning and follow-up. Therefore, processing digital health data with AI could support the delivery of effective and efficient healthcare. Nonetheless, even though the advancement of AI carries much potential, what value AI can and will deliver in actual clinical practice remains a fundamental question. Therefore, this research aims to generate and substantiate evidence supporting clinical effectiveness, specifically comparative effectiveness, cost-effectiveness or other formal health technology assessment (HTA) of AI in a clinical healthcare setting in oncology.


Opportunities for personalised follow-up in breast cancer: the gap between daily practice and recurrence riskBreast cancer research and treatment, 205, 313-322 (E-pub ahead of print/First online). Voets, M., Hassink, N., Veltman, J., Slump, C. H., Koffijberg, H. & Siesling, S.https://doi.org/10.1007/s10549-024-07246-5Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trialsCancer Imaging, 24(48), Article 48, 1-10 (E-pub ahead of print/First online). Alaeikhanehshir, S., Voets, M., van Duijnhoven, F. H., Lips, E. H., Groen, E. J., van Oirsouw, M. C. J., Hwang, S. E., Lo, J. Y., Wesseling, J., Mann, R. M. & Teuwen, J.https://doi.org/10.1186/s40644-024-00691-x



Universiteit Twente

Technohal (gebouwnr. 18), kamer 3106
Hallenweg 5
7522 NH Enschede

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