  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.
