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FairDiffusion: Enhancing equity in latent diffusion models via fair Bayesian perturbation.

Yan Luo1,2,3, Muhammad Osama Khan4, Congcong Wen4,5

  • 1Harvard AI and Robotics Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114 USA.

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|April 4, 2025
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Summary
This summary is machine-generated.

Generative AI in healthcare shows bias in image generation across demographics. A new model, FairDiffusion, and dataset, FairGenMed, aim to improve fairness and quality for equitable AI benefits.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Generative AI, especially diffusion models, excels at text-to-image synthesis.
  • These models hold promise for synthetic data generation and medical training in healthcare.
  • Concerns exist regarding the consistency of image generation quality across demographic subgroups.

Purpose of the Study:

  • To conduct a comprehensive analysis of fairness in medical text-to-image diffusion models.
  • To propose and evaluate an equity-aware model to mitigate identified biases.
  • To introduce a new dataset for studying fairness in medical generative models.

Main Methods:

  • Evaluated the Stable Diffusion model for demographic disparities in image generation.
  • Developed FairDiffusion, an equity-aware latent diffusion model.
  • Curated FairGenMed, a dataset specifically for fairness studies in medical generative AI.
  • Assessed FairDiffusion on dermatoscopic images (HAM10000) and chest X-rays (CheXpert).

Main Results:

  • Identified significant disparities in Stable Diffusion's image generation across gender, race, and ethnicity.
  • FairDiffusion demonstrated improved image quality and semantic alignment of clinical features.
  • The model proved effective across diverse medical imaging modalities.

Conclusions:

  • Fairness is a critical consideration for generative AI in healthcare.
  • FairDiffusion and FairGenMed represent advancements in fair generative learning.
  • These contributions promote equitable applications of generative AI in the medical field.