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

<h3>Context:</h3> Traditional clinical health datasets often fail to represent the diversity and breadth of real-world disease, influencing research and AI tools developed on those datasets. Dermatology, easily documentable through patient-contributed images, presents an ideal case for innovative dataset creation. <h3>Objective:</h3> To develop a scalable, representative dermatology dataset using crowdsourcing via internet search ads, enhancing medical education and AI application development. <h3>Study Design and Analysis:</h3> We employed Google Search ads to gather dermatology images from the public, with informed consent, between March and November 2023. Images were curated, de-identified, and labeled by dermatologists, providing a dataset of 10,408 images from 5,033 contributors across the United States. <h3>Setting or Dataset:</h3> The dataset aggregated includes diverse skin conditions and demographic data, now publicly accessible on GitHub. <h3>Population Studied:</h3> Internet users across the United States, representing various demographics and skin types, contributed to the dataset. <h3>Intervention/Instrument:</h3> Google Search ads targeted individuals searching for skin-related terms, inviting them to contribute images and related information via a web platform. <h3>Outcome Measures:</h3> Measures included the volume of contributions, demographic diversity, and the diagnostic usability of images as evaluated by dermatologists. <h3>Results:</h3> The study achieved a median of 22 submissions per day, with a significant representation of diverse skin conditions and demographics. Over 97.5% of contributions were usable, with high dermatologist confidence correlated with the completeness of accompanying data. <h3>Conclusions:</h3> Crowdsourcing via search ads effectively generates diverse, representative dermatological datasets. The SCIN dataset can significantly enhance dermatological research, education, and AI tool accuracy, particularly in underrepresented communities.