Why federated learning will do little to overcome the deeply embedded biases in clinical medicine is a research paper published in Intensive Care Medicine (2024). On theSindex it has a DataRank of 0. It has been cited 6 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
UMEA Clinician Scientist/ DFG
Grant: FU356/12-2
Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen
Grant: CANTAR Netzwerke 2021
National Institute of Health
Grant: R01 EB017205
National Institute of Health
Grant: DS-I Africa U54 TW012043-01
National Institute of Health
Grant: Bridge2AI OT2OD032701
National Science Foundation
Grant: ITEST #2148451
NIH HHS
Grant: OT2 OD032701
FIC NIH HHS
Grant: U54 TW012043
National Institutes of Health
Grant: 3R01EB017205-06S1
Critical Care Informatics: Ethical considerations around the use and sharing of health-related data
National Science Foundation
Grant: 2148451
A Learning Ecosystem for Teaching High School Students Machine Learning Concepts and Data Science Skills in Healthcare and Medicine
National Institutes of Health
Grant: 3OT2OD032701-01S3
Bridge2AI: Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI
National Institutes of Health
Grant: 5U54TW012043-05
MUST Data Science Research Hub (MUDSReH)
Universitätsklinikum Essen
FWCI
1.66
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals