Data Science for Medical Informatics

Dear Colleagues,

Medical data science is a rapidly growing field and will transform healthcare and medical research. Despite the presence of a large spectrum of analysis methods, including powerful machine learning algorithms, unavailability and low quality of data is the biggest barrier for developing successful AI based medical applications.

Most of the medical data still resides in non- digital form or as text, or poorly documented and can not be semantically interpreted. FAIR (Findable, Accessible, Interoperable, Reusable) data is the key for closing the gap between advancement in machine learning and translation of AI in clinics.  The FAIR data management and analysis leads to reproducible, interpretable, transferable  data science, as well as having a positive impact on data quality.

This special issue addresses this gap by collecting best FAIR data practices in medical data science.  We welcome submissions presenting new approaches and methods for improving FAIR data and quality, reproducible and privacy preserved analyses, as well as success stories for translation and impact of AI in medical practice. 

Prof. Dr. Toralf Kirsten
Prof. Dr. Oya Beyan
Guest Editors