⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

May 3, 2026International Journal of Dermatology

Diagnostic Accuracy of Three Large Language Models on Clinical Images Varies by Skin Tone: Findings From the Stanford Diverse Dermatology Images Dataset

Eunice Y. Ju Ahana Sinharoy Nicole Edmonds R Hal Flowers

View at Publisher

Ask AI

Bookmark

Share

Key Points

This research aims to evaluate how accurately three large language models diagnose clinical images across different skin tones.
Used the Stanford Diverse Dermatology Images dataset to assess the models.
Analyzed diagnostic accuracy across various skin tones.
Data is openly available for further examination.
Diagnostic performance varied significantly by skin tone, with some tones receiving less accuracy.
The findings highlight a potential bias in model predictions.
Implications suggest a need for more diverse training data in AI.

No abstract yet

We only have this paper's title and publication details.

Read it at the source→

Implication

Evaluates diagnostic accuracy of language models in dermatology based on skin tone, indicating disparities in performance.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Ju et al. (2026) studied this question.

synapsesocial.com/papers/69fc2b608b49bacb8b3478f6 https://doi.org/10.1111/ijd.70452

View at Publisher

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Disparities in dermatology AI performance on a diverse, curated clinical image setOriginal Research · 2022 · 534 citations
2Adapting Large Language Models to Mitigate Skin Tone Biases in Clinical Dermatology Tasks: A Mixed-Methods Study2025 · 1 citations
3DDI-CoCo: A Dataset for Understanding the Effect of Color Contrast in Machine-Assisted Skin Disease Detection2024 · 11 citations
4Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation2024
5Investigating the Performance of VisualDx on Common Dermatologic Conditions in Skin of Color2024 · 1 citations

More related papers

A Retrospective Analysis of a Dermatology-Trained Multimodal Large Language Model's Diagnostic Accuracy in Pigmented Skin Lesions2025
Equity and Generalizability of Artificial Intelligence for Skin-Lesion Diagnosis Using Clinical, Dermoscopic, and Smartphone Images: A Systematic Review and Meta-Analysis2025
Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology2025

More from International Journal of Dermatology

The Differential That Never Gets GeneratedWed,
Nevus Counts, Sun Exposure and Phenotypic Characteristics in Invasive Versus In Situ Non‐Lentigo Maligna/Non‐Acral Lentiginous MelanomaThu,