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Creating an Empirical Dermatology Dataset Through Crowdsourcing With Web Search Advertisements.
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Creating an Empirical Dermatology Dataset Through Crowdsourcing With Web Search Advertisements. JAMA network open Ward, A., Li, J., Wang, J., Lakshminarasimhan, S., Carrick, A., Campana, B., Hartford, J., Sreenivasaiah, P. K., Tiyasirisokchai, T., Virmani, S., Wong, R., Matias, Y., Corrado, G. S., Webster, D. R., Smith, M. A., Siegel, D., Lin, S., Ko, J., Karthikesalingam, A., Semturs, C., Rao, P. 2024; 7 (11): e2446615Abstract
Health datasets from clinical sources do not reflect the breadth and diversity of disease, impacting ÌÇÐÄ´«Ã½, medical education, and artificial intelligence tool development. Assessments of novel crowdsourcing methods to create health datasets are needed.To evaluate if web search advertisements (ads) are effective at creating a diverse and representative dermatology image dataset.This prospective observational survey study, conducted from March to November 2023, used Google Search ads to invite internet users in the US to contribute images of dermatology conditions with demographic and symptom information to the Skin Condition Image Network (SCIN) open access dataset. Ads were displayed against dermatology-related search queries on mobile devices, inviting contributions from adults after a digital informed consent process. Contributions were filtered for image safety and measures were taken to protect privacy. Data analysis occurred January to February 2024.Dermatologist condition labels as well as estimated Fitzpatrick Skin Type (eFST) and estimated Monk Skin Tone (eMST) labels.The primary metrics of interest were the number, quality, demographic diversity, and distribution of clinical conditions in the crowdsourced contributions. Spearman rank order correlation was used for all correlation analyses, and the ?2 test was used to analyze differences between SCIN contributor demographics and the US census.In total, 5749 submissions were received, with a median of 22 (14-30) per day. Of these, 5631 (97.9%) were genuine images of dermatological conditions. Among contributors with self-reported demographic information, female contributors (1732 of 2596 contributors [66.7%]) and younger contributors (1329 of 2556 contributors [52.0%] aged <40 years) had a higher representation in the dataset compared with the US population. Of 2614 contributors who reported race and ethnicity, 852 (32.6%) reported a racial or ethnic identity other than White. Dermatologist confidence in assigning a differential diagnosis increased with the number of self-reported demographic and skin-condition-related variables (Spearman R?=?0.1537; P?
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