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FairGen: preference-aligned diffusion for demographically equitable medical image synthesis.

Zhimin Li1, Ruichen Zhang2, Zhen Tan3

  • 1Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.

NPJ Digital Medicine
|June 13, 2026
PubMed
Summary

FairGen, a new AI framework, creates balanced medical images to reduce diagnostic bias in AI models. This approach enhances fairness and accuracy across diverse demographic groups in medical imaging analysis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Healthcare Equity

Background:

  • Demographic imbalances in clinical image data can lead to AI diagnostic bias.
  • Diseases manifest differently across demographics, creating rare presentations in AI training data.
  • AI models trained on imbalanced data risk perpetuating healthcare disparities.

Purpose of the Study:

  • Introduce FairGen, a fairness-aware diffusion framework for synthesizing demographically balanced medical images.
  • Improve subgroup coverage and downstream classification accuracy in AI models.
  • Mitigate diagnostic bias and enhance healthcare equity in medical AI.

Main Methods:

  • Developed FairGen, a diffusion framework embedding physician preferences for balanced image synthesis.
  • Preserved pathology-relevant visual features during image generation.
  • Applied FairGen to dermatology, radiology, and neuroimaging datasets.

Main Results:

  • Achieved significant fairness improvements: 95.9% for skin images, 80.0% for chest radiography, and 35.2% for brain MRI.
  • Maintained competitive diagnostic accuracy compared to models trained on original data.
  • Expert review and external validation confirmed extended gains beyond standard fidelity metrics.

Conclusions:

  • FairGen effectively synthesizes demographically balanced medical images, reducing AI bias.
  • The framework enhances fairness and diagnostic accuracy across diverse medical imaging applications.
  • FairGen represents a significant step towards equitable AI in healthcare.