The growing integration of artificial intelligence into biosciences and healthcare is reshaping innovation processes while simultaneously amplifying risks related to safety, accountability, and trust. As AI systems increasingly influence decisions with significant biological and health impacts, questions of responsibility can no longer be addressed solely at the technical level.
This...
The rapid integration of artificial intelligence into biosciences and clinical diagnostics has transformed contemporary healthcare, offering improved accuracy, efficiency, and predictive capacity. At the same time, the growing reliance on machine learning systems,particularly opaque “black box” models raises significant legal concerns regarding transparency, accountability, and patient...
Next‑Gen Therapies & Fair Health Innovation - equity, long‑term safety, and regulation of gene editing, mRNA, and cell‑based therapies.
Background. In psychiatry, there currently are attempts to develop predictive models identifying risk for severe mental illness before any clinical symptoms appear, thus enabling early intervention and reduction of long-term burden of mental illness. Since these processes need to be responsible and include target group perspective (people with the experience of mental health issues),...
This research investigates deep learning approaches for the automated detection and segmentation of aortic aneurysms from CT imaging. The project initially focuses on the development of a custom deep learning pipeline designed for medical image preprocessing, training, and evaluation. The baseline model used in this pipeline is the widely adopted U-Net, which has become a standard architecture...
The rapid integration of Artificial Intelligence (AI) into higher education is reshaping pedagogical practices, learning environments, and academic expectations. While AI-powered tools promise efficiency, personalization, and enhanced access to knowledge, their widespread adoption also raises important questions about student wellbeing, cognitive overload, and the evolving relationship between...
Aortic aneurysms pose a significant clinical risk due to the potential for rupture, a life-threatening event whose likelihood depends on the hemodynamic forces acting on the arterial wall. Quantities such as wall shear stress, intraluminal pressure, and wall displacement are critical indicators of rupture risk, yet their accurate computation requires solving coupled partial differential...
I introduce and study a class of neural network operators whose activation
mechanism is built from cardinal B-splines. The compact support and smoothness
of B-splines lead to localized approximation processes that fit naturally
into the theory of positive operators. I prove convergence results and investigate
qualitative features such as shape preservation, showing that several
geometric...
