arXiv (All)
open access
Disease Burden over Skin Tone: Decomposing the Dermatology-AI Generalization Gap
Dermatology artificial intelligence (AI) models are predominantly trained on light-skinned, cancer-focused image collections, yet they are increasingly proposed for deployment in resource-constrained settings where patients differ from training populations along two confounded axes: skin tone and di
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Image 1_Machine learning models for predicting liver cancer: a real-world cohort study in China.tiff#669783
- Evaluation of a Non-Invasive Device for Early Detection of Atopic Dermatitis Flares#899836
- Age-related prognostic disparities in cutaneous melanoma: A population-based study and validated interactive web-based nomogram for individualized prognostic stratification.#458654
- Extended Gamma Distribution to Fitting Breast Tumors#1002656
- Development of an Optimal Algorithm for the Management of Patients With Retinal Pigment Epithelium Detachment in Neovascular Age-related Macular Degeneration Using Artificial Intelligence#882216
Record · ID 822513
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive