Crowdsourcing Dermatology Images with Google Search Ads: Creating a Real-World Skin Condition Dataset for AI Development
Year: 2024
Type: conference-paper
Abstract: Context: 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. Objective: To develop a scalable, representative dermatology dataset using crowdsourcing via internet search ads, enhancing medical education and AI application development. Study Design and Analysis: 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. Setting or Dataset: The dataset aggregated includes diverse skin conditions and demographic data, now publicly accessible on GitHub. Population Studied: Internet users across the United States, representing various demographics and skin types, contributed to the dataset. Intervention/Instrument: Google Search ads targeted individuals searching for skin-related terms, inviting them to contribute images and related information via a web platform. Outcome Measures: Measures included the volume of contributions, demographic diversity, and the diagnostic usability of images as evaluated by dermatologists. Results: 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. Conclusions: 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.
Authors: Yejin Jeong, Mike Schaekermann, Steven Lin
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4404838365