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

StudyNPJ digital medicine· Jun 2026

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

Zhimin Li, Ruichen Zhang, Zhen Tan … Tianlong Chen

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DermatologyStudyQ1IF 18.0

FairGen made skin image synthesis far more demographically fair while keeping accuracy

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

Zhimin Li … Tianlong Chen

NPJ digital medicine · Jun 2026 · Q1 in General Medicine

Background

Medical imaging is central to modern diagnostics, and artificial intelligence (artificial intelligence) systems are increasingly used to support image-based analysis by improving efficiency, accuracy, and access to care.

Results

Fairness across demographic groups improved 95.9% for skin images, accuracy maintained

95.9%

skin images

80%

chest radiography

35.2%

brain MRI

More results

However, inequities in healthcare access and differential disease prevalence create severe demographic imbalances in clinical image data.

Artificial intelligence models trained on such imbalanced data risk perpetuating diagnostic bias and widening healthcare disparities.

“

Conclusion

Clinician-facing expert review and external validation on independent cohorts further support that these gains extend beyond standard fidelity metrics and are not confined to the original in-distribution datasets.

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

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

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