As a first post I would like to not highlight my own research, but the research of my colleagues, since I really love the way how this research began: With a question many people have asked.
As our working group researches on topics on #anonymization and #deidentification of medical and health data, the risk of identifying people or their membership in certain groups (e.g. having a specific diagnosis) is always present. On one of our retreats they decided to research the question:
Who are those adversaries, trying to do re-identify people in health data sets, what are their motives and what could be the harm?
The results can be found in the #OpenAccess paper: Health Data Re-Identification: Assessing Adversaries and Potential Harms
Applying AI to support categorisation of heterogeneous epidemiological datasets
Programme: 
Wissenschaftliche*r Mitarbeiter*in (w/m/d) der Fachrichtung (Medizin-) Informatik 
Can structured data standards solve the biggest barriers in modern physical rehabilitation?
CSBJ Smart Hospital: 
Can we decode how radiologists think — so machines can think with them?
This study explores how Federated Learning (FL) can revolutionize medical AI by enhancing generalizability while preserving patient privacy. 
iStockphoto / gorodenkoff
13-16 February 2023 in @BaselStadt 
