Towards Fairer AI in Medical Text Generation

Xiuying Chen and colleagues address a critical blind spot in AI-driven healthcare: bias in medical text generation. While fairness has been widely studied in medical imaging, text generation remains comparatively underexplored. Their study reveals consistent disparities in generated outputs across demographic dimensions such as race, sex, age, and intersectional subgroups. These discrepancies persist across various model architectures, sizes, and benchmarks. To tackle this, the team introduces a bias-mitigation algorithm that strategically boosts performance for underserved populations without degrading overall model accuracy. Their approach represents a significant step toward equitable AI in clinical communication, highlighting the need for fairness-aware optimization in generative medical applications.