Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity

Background: Discrepancies in medical data sets can perpetuate bias, especially when training deep learning models, potentially leading to biased outcomes in clinical applications. Understanding these biases is crucial for the development of equitable healthcare technologies. This study employs gener...

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Bibliographic Details
Main Authors:	Bardia Khosravi, MD, MPH, MHPE, Pouria Rouzrokh, MD, MPH, MHPE, Bradley J. Erickson, MD, PhD, Hillary W. Garner, MD, Doris E. Wenger, MD, Michael J. Taunton, MD, Cody C. Wyles, MD
Format:	Article
Language:	English
Published:	Elsevier 2024-10-01
Series:	Arthroplasty Today
Subjects:	Generative AI Explainability Dataset curation Equity Bias
Online Access:	http://www.sciencedirect.com/science/article/pii/S2352344124001882
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Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity

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