Fairness of AI in Medical Imaging

Research on fairness, equity, and accountability in the context of machine learning has extensively demonstrated ethical risks in the deployment of such systems, including for medical image analysis. In a series of interdisciplinary events, our aim is to advance the discourse around fairness issues in the medical image analysis community.

Next up: ISBI 2024 tutorial Fairness of AI in Medical Imaging (FAIMI)!

Organizers

Aasa Feragen, DTU Compute, Technical University of Denmark
Andrew King, King’s College London
Ben Glocker, Imperial College London
Daniel Moyer, Vanderbilt University
Enzo Ferrante, CONICET, Universidad Nacional del Litoral
Eike Petersen, DTU Compute, Technical University of Denmark
Esther Puyol-Antón, HeartFlow and King’s College London
Melanie Ganz-Benjaminsen, University of Copenhagen & Neurobiology Research Unit, Rigshospitalet
Veronika Cheplygina, IT University Copenhagen

Advisory Panel members

Judy Gichoya, Emory University, USA
Kanwal Bhatia, Aival, UK
Tal Arbel, McGill University, USA
Bishesh Khanal, NAAMII, Nepal
Ira Ktena, Deep Mind, UK
Sanmi Koyejo, Stanford University, USA
Karim Lekadir, Universitat de Barcelona, Spain

Please direct any inquiries related to this initiative to faimi-organizers@googlegroups.com.