The Data Artifacts Glossary: a community-based repository for bias on health datasets
The Data Artifacts Glossary serves as a vital resource for enhancing the integrity of AI applications in healthcare by providing a mechanism to recognize and mitigate dataset biases before they impact AI outputs. It not only aids in avoiding bias in model development but also contributes to understanding and addressing the root causes of health disparities.
Authors
- Chrystinne Oliveira Fernandes (ORCID: https://orcid.org/0000-0002-8623-9500)
- Leo Anthony Celi (ORCID: https://orcid.org/0000-0001-6712-6626)
- An-Kwok Ian Wong (ORCID: https://orcid.org/0000-0001-5668-4251)
- Sicheng Hao (ORCID: https://orcid.org/0000-0002-8905-005X)
- Christopher Martin Sauer (ORCID: https://orcid.org/0000-0002-2388-5919)
- Naira Link Woite (ORCID: https://orcid.org/0000-0003-3145-7903)
- Anna E. Premo (ORCID: https://orcid.org/0009-0003-3067-3433)
- Alice Rangel Teixeira (ORCID: https://orcid.org/0000-0001-5034-1223)
- Rodrigo R. Gameiro
- Isabelle Resli
Institutions
- Universitat Autònoma de Barcelona (ES)
- Oregon State University (US)
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
- University of Pittsburgh (US)
- Duke University (US)
- Cancer Research And Biostatistics (US)
- Essen University Hospital (DE)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Journal of Biomedical Science
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1186/s12929-024-01106-6
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.31