Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool
Abstract Medical datasets are vital for advancing Artificial Intelligence (AI) in healthcare. Yet biases in these datasets on which deep-learning models are trained can compromise reliability. This study investigates biases stemming from dataset-creation practices. Drawing on existing guidelines, we...
Saved in:
Main Authors: | Maria Galanty, Dieuwertje Luitse, Sijm H. Noteboom, Philip Croon, Alexander P. Vlaar, Thomas Poell, Clara I. Sanchez, Tobias Blanke, Ivana Išgum |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-12-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-024-83218-5 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bias in mobility datasets drives divergence in modeled outbreak dynamics
by: Taylor Chin, et al.
Published: (2025-01-01) -
The Data Artifacts Glossary: a community-based repository for bias on health datasets
by: Rodrigo R. Gameiro, et al.
Published: (2025-02-01) -
A large-scale dataset for Chinese historical document recognition and analysis
by: Yongxin Shi, et al.
Published: (2025-01-01) -
PatCID: an open-access dataset of chemical structures in patent documents
by: Lucas Morin, et al.
Published: (2024-08-01) -
Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets
by: Konstantin Sharafutdinov, et al.
Published: (2024-01-01)