From the 24th till the 28th of January, the LUMC organized the Data Ethics winter school on behalf of the Eurolife consortium, an international network consisting of 9 European universities. The network focuses predominantly on biomedical sciences, but also related subjects in the field of Life Sciences & Health. We are very proud of the amazing success of the winter school and the exciting responses we’ve had from students. In this article we would like to share the experience of one of the participating students, Jonneke Bouwhuis.
“In January 2022, I participated in the Data Ethics Online winter school. This winter school dealt with the rapid developments of big data and A.I. in the field of clinical practice and medical research. Some of the main themes of this week were: Medical ethics, A.I. ethics, Informed consent, Data ownership and responsibility, and Modeling techniques
These themes were elaborated on by lecturers from all over Europe. Some of the contributing universities were the LUMC, Trinity College Dublin, University of Barcelona, Medical University of Innsbruck, UMIT, Karolinska Institutet and IHU Strasbourg. Not only the lecturers, but also the participating students had a diverse background when looking at their home countries and the universities they are currently attending. The combination of a diverse panel of lecturers and students made for a great place for discussion.
During the week, the VaKE learning method was applied: lecturers provided a starting point and presented certain topics, after which the students could discuss and investigate further to develop their own knowledge and point of view on a central dilemma surrounding implementing and using A.I. models in clinical practice presented during the week. The goal of this approach was to develop sensitivity for ethical issues in data collection, data storage and data ownership, as well as possible sources of bias in A.I. models and the resulting implications in clinical practice and research.
My favorite part of this week was the input from patients and clinicians. They had very specific examples of possibilities and current delays in the development of A.I., while also being very aware of the possible negative implications A.I. could bring about. They were able to provide a balanced overview of the current state of A.I. models in healthcare and pointed out some wishes/demands to consider during the development of these models, which was a very useful addition to the ethical experts and A.I. experts who provided the main part of the lectures and literature.
Overall, I would recommend anyone in the (bio-)medical or (bio-)pharmaceutical field to explore the possibilities of A.I. models and the ethical implications that follow the application of A.I. This winter school was a great starting point for the knowledge, critical thinking, and network you need, and I would urge anyone to keep an eye out for the lectures online when they get published on the Eurolife website.”
The African-European Tuberculosis Consortium (AE-TBC) is an international multisite group of African and European researchers who investigate the use of host biosignatures for the diagnosis of active TB disease. For over ten years, the LUMC groups of Infectious Diseases and Cell & Chemical Biology from Prof Annemieke Geluk and Dr Paul Corstjens respectively, have been partners of the EDCTP consortia for tuberculosis (among which AE-TBC). The AE-TBC recently won the prize for ‘Outstanding Research Team 2020, awarded by the EDCTP.
The Janssen-Cilag International N.V COVID-19 vaccine has received authorization for emergency use by the European Medicines Agency (EMA) on March 11. Developed with fundamental support from the Molecular Virology group of the Leiden University Medical Centre (LUMC), it is the fourth vaccine to be administered in the European Union. The Netherlands has ordered more than 11 million vaccine doses.
“This is an achievement in alignment with the mission of VODAN-Africa to generate continuous, real-time, high velocity clinical observational patient data from resource-limited communities that have not been well represented in digital health data. The key feature is that the data produced remains in the health facility only. It will not leave the health facility. Since the data is machine-actionable the input of the data only happens once; in the deployable architecture, the data is used for four parallel use cases” (VODAN to Africa, 2022).