# Crowdsourcing Dermatology Images with Google Search Ads: Creating a Real-World Skin Condition Dataset

arXiv: https://arxiv.org/abs/2402.18545 · HTML: https://mlsanity.com/paper/2402.18545/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2024.

## Built on by (most cited first, 21 of 21)

- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology](https://mlsanity.com/paper/2503.14911/) (2025)
- [3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark](https://mlsanity.com/paper/2504.13861/) (2025)
- [MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from Textbooks](https://mlsanity.com/paper/2505.06152/) (2025)
- [Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback](https://mlsanity.com/paper/2506.12323/) (2025)
- [eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases](https://mlsanity.com/paper/2508.18608/) (2025)
- [DermoGPT: Open Weights and Open Data for Morphology-Grounded Dermatological Reasoning MLLMs](https://mlsanity.com/paper/2601.01868/) (2026)
- [DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research](https://mlsanity.com/paper/2506.06099/) (2025)
- [DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model](https://mlsanity.com/paper/2508.12190/) (2025)
- [TrueSkin: Towards Fair and Accurate Skin Tone Recognition and Generation](https://mlsanity.com/paper/2509.10980/) (2025)
- [Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology](https://mlsanity.com/paper/2605.04098/) (2026)
- [MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models](https://mlsanity.com/paper/2607.07673/) (2026)
- [Anisotropic Diffusion Probabilistic Model for Imbalanced Image Classification](https://mlsanity.com/paper/2409.14313/) (2024)
- [Adapting Large Language Models to Mitigate Skin Tone Biases in Clinical Dermatology Tasks: A Mixed-Methods Study](https://mlsanity.com/paper/2510.00055/) (2025)
- [LesionTABE: Equitable AI for Skin Lesion Detection](https://mlsanity.com/paper/2601.03090/) (2026)
- [Establishing dermatopathology encyclopedia DermpathNet with Artificial Intelligence-Based Workflow](https://mlsanity.com/paper/2601.19378/) (2026)
- [Colorimeter-Supervised Skin Tone Estimation from Dermatoscopic Images for Fairness Auditing](https://mlsanity.com/paper/2602.10265/) (2026)
- [Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification](https://mlsanity.com/paper/2603.02475/) (2026)
- [Picking the Right Image to Classify: Reliable-Input Selection in Teledermatology](https://mlsanity.com/paper/2608.16198/) (2026)
- [Beyond Blur: A Semantic Tri-view Pipeline for Teledermatology Gradability via Skin Micro-relief](https://mlsanity.com/paper/2609.03095/) (2026)
- [Benchmarking Off-the-Shelf Multimodal AI Models Against Dermatologists on Patient-Captured Skin Images](https://mlsanity.com/paper/2609.24190/) (2026)

## Abstract (arXiv)

Background: Health datasets from clinical sources do not reflect the breadth and diversity of disease in the real world, impacting research, medical education, and artificial intelligence (AI) tool development. Dermatology is a suitable area to develop and test a new and scalable method to create representative health datasets. Methods: We used Google Search advertisements to invite contributions to an open access dataset of images of dermatology conditions, demographic and symptom information. With informed contributor consent, we describe and release this dataset containing 10,408 images from 5,033 contributions from internet users in the United States over 8 months starting March 2023. The dataset includes dermatologist condition labels as well as estimated Fitzpatrick Skin Type (eFST) and Monk Skin Tone (eMST) labels for the images. Results: We received a median of 22 submissions/day (IQR 14-30). Female (66.72%) and younger (52% < age 40) contributors had a higher representation in the dataset compared to the US population, and 32.6% of contributors reported a non-White racial or ethnic identity. Over 97.5% of contributions were genuine images of skin conditions. Dermatologist confidence in assigning a differential diagnosis increased with the number of available variables, and showed a weaker correlation with image sharpness (Spearman's P values <0.001 and 0.01 respectively). Most contributions were short-duration (54% with onset < 7 days ago ) and 89% were allergic, infectious, or inflammatory conditions. eFST and eMST distributions reflected the geographical origin of the dataset. The dataset is available at github.com/google-research-datasets/scin . Conclusion: Search ads are effective at crowdsourcing images of health conditions. The SCIN dataset bridges important gaps in the availability of representative images of common skin conditions.