The subtleties of abolishing "race correction" in clinical artificial intelligence.

Key Points

• The aim is to explore the implications of removing race correction in clinical artificial intelligence systems.
• Identified necessary interventions for race correction removal
• Engaged diverse stakeholders in discussions
• Developed strategies for enhancing model transparency and auditability
• Emphasized the importance of context in implementing race correction abolition
• Highlighted stakeholder engagement as critical for effective change
• Showed that enhanced transparency can lead to more trustworthy AI models