Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity
This study demonstrates the potential of generative models for understanding disparities in medical imaging data sets. By visualizing race-based differences, this method aids in identifying bias in downstream tasks, fostering the development of fairer healthcare practices.
Authors
- Bradley J. Erickson (ORCID: https://orcid.org/0000-0001-7926-6095)
- Hillary W. Garner (ORCID: https://orcid.org/0000-0002-6699-9692)
- Michael J. Taunton (ORCID: https://orcid.org/0000-0002-7636-0489)
- Cody C. Wyles (ORCID: https://orcid.org/0000-0002-8629-7567)
- Bardia Khosravi (ORCID: https://orcid.org/0000-0002-8024-339X)
- Doris E. Wenger (ORCID: https://orcid.org/0000-0003-2191-4840)
- Pouria Rouzrokh
Institutions
- WinnMed (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Arthroplasty Today
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1016/j.artd.2024.101503
- Citations
- 13
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.56
Funders
- Mayo Foundation for Medical Education and Research