PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa
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Abstract (arXiv)
Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatric population suffering from largely untreated skin conditions. The integration of AI into healthcare sparks significant hope for treatment accessibility, especially through the development of AI-supported teledermatology. Current AI models are predominantly trained on white-skinned patients and do not generalize well enough to pigmented patients. The PASSION project aims to address this issue by collecting images of skin diseases in Sub-Saharan countries with the aim of open-sourcing this data. This dataset is the first of its kind, consisting of 1,653 patients for a total of 4,901 images. The images are representative of telemedicine settings and encompass the most common paediatric conditions: eczema, fungals, scabies, and impetigo. We also provide a baseline machine learning model trained on the dataset and a detailed performance analysis for the subpopulations represented in the dataset. The project website can be found at https://passionderm.github.io/.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 7 Nov 2024.
Builds on
9 of 32 references.
References · 9
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Built on by
Citing papers per year: 2025: 4; 2026: 1.
5 papers.
- 2025Are generative models fair? A study of racial bias in dermatological image generationcited by 7
- 2025DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Researchcited by 2
- 2025Understanding-informed Bias Mitigation for Fair CMR Segmentationcited by 1
- 2025Bayesian generative models can flag performance loss, bias, and out-of-distribution image contentcited by 0
- 2026Picking the Right Image to Classify: Reliable-Input Selection in Teledermatologycited by 0
- 2026Picking the Right Image to Classify: Reliable-Input Selection in Teledermatology17 Aug 2026
- 2025DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research6 Jun 2025
- 2025Bayesian generative models can flag performance loss, bias, and out-of-distribution image content21 Mar 2025
- 2025Understanding-informed Bias Mitigation for Fair CMR Segmentation21 Mar 2025
- 2025Are generative models fair? A study of racial bias in dermatological image generation20 Jan 2025