mlsanityData to 14 Oct 2026

Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Images

arXiv 2504.01838 ↗First posted 2 Apr 2025cs.CVcited by 1

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

Artificial Intelligence (AI) in skin disease diagnosis has improved significantly, but a major concern is that these models frequently show biased performance across subgroups, especially regarding sensitive attributes such as skin color. To address these issues, we propose a novel generative AI-based framework, namely, Dermatology Diffusion Transformer (DermDiT), which leverages text prompts generated via Vision Language Models and multimodal text-image learning to generate new dermoscopic images. We utilize large vision language models to generate accurate and proper prompts for each dermoscopic image which helps to generate synthetic images to improve the representation of underrepresented groups (patient, disease, etc.) in highly imbalanced datasets for clinical diagnoses. Our extensive experimentation showcases the large vision language models providing much more insightful representations, that enable DermDiT to generate high-quality images. Our code is available at https://github.com/Munia03/DermDiT

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 19 of its 32 references that are in the map. First posted on arXiv 2 Apr 2025.

Builds on

19 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day1 Jun

Other references · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2023Visual Instruction Tuningcited by 7,869
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  5. 2022Scalable Diffusion Models with Transformerscited by 6,054
  6. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  7. 2022MedCLIP: Contrastive Learning from Unpaired Medical Images and Textcited by 570
  8. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 185
  9. 2020A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Contextcited by 146
  10. 2021Disparities in Dermatology AI: Assessments Using Diverse Clinical Imagescited by 134
  11. 2022FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosiscited by 59
  12. 2018The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesionscited by 26
6 more
  1. 2022Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classificationcited by 23
  2. 2023FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classificationcited by 19
  3. 2023Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosiscited by 14
  4. 2022The (de)biasing effect of GAN-based augmentation methods on skin lesion imagescited by 9
  5. 2024Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classificationcited by 5
  6. 2025DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosiscited by 4

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
  2. 2023Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts
  3. 2025Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
  4. 2023Augmenting medical image classifiers with synthetic data from latent diffusion models
  5. 2024Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology
  6. 2025SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation in Melanoma Diagnosis
  7. 2026A Vision-Language Foundation Model for Zero-shot Clinical Collaboration and Automated Concept Discovery in Dermatology
  8. 2022Improving dermatology classifiers across populations using images generated by large diffusion models