Background/Objectives: Digital technologies and data-driven approaches are rapidly transforming healthcare practice and enabling more personalized and preventive care. As personal health data becomes increasingly embedded in healthcare systems, understanding how future healthcare professionals interpret …

Uitlegbaarheid van AI in de praktijk: implementatie van de uitlegbaarheidseisen van de EU-AI-verordening door AI-aanbieders in de landbouwsector

\emph{Purpose.} Tracheostomy is a critical, life-saving procedure, but complications often arise due to suboptimal tracheostomy tube fit. Customised tubes could mitigate these risks, yet their development hinges on precise tracheal morphometric data. This study aims to establish an automated workflow …

Explainability of AI systems is branded as a solution to the black box problem. The EU AI Act introduces AI explainability as a requirement for high-risk AI systems, including a right to explanation. However, understanding the wording of the requirements and the related provider and deployer obligations …

During BILETA Conference I presented my upcoming paper about AI LIteracy and the challanges of adoption across the EU, in light of the upcoming EU Omnibus Digital Package.

Presentatie en workshop binnen het event 'AI en wij - een dag voor alle Minerva docenten'

Abstract from EHRCON25-openEHR International Conference 2025. The increasing demand for structured high-quality clinical data for AI and decision support systems highlights the shortcomings of current healthcare data infrastructure, particularly its misalignment with FAIR [1] data principles (Findable, …

Overal om ons heen nemen algoritmen en AI-systemen beslissingen die mensen raken. In het onderwijs, bij de overheid, in dienstverlening. Vaak gaat het gesprek over wat technisch kan en wat juridisch mag. Maar een cruciale vraag blijft te vaak liggen: wat willen we eigenlijk – en voor wie ontwerpen we …

Efficient hydrogen (H2) storage remains a major challenge for clean energy applications. This study presents an AI-driven methodology to optimize H2 storage in porous carbon adsorbents. A comprehensive dataset of 917 literature-derived entries was used to develop two machine learning models: Random Forest …

How designers can contribute to responsible AI in the clinic