title: Crowdsourcing in biomedicine: challenges and opportunities.
abstract: The use of crowdsourcing to solve important but complex problems in biomedical and clinical sciences is growing and encompasses a wide variety of approaches. The crowd is diverse and includes online marketplace workers, health information seekers, science enthusiasts and domain experts. In this article, we review and highlight recent studies that use crowdsourcing to advance biomedicine. We classify these studies into two broad categories: (i) mining big data generated from a crowd (e.g. search logs) and (ii) active crowdsourcing via specific technical platforms, e.g. labor markets, wikis, scientific games and community challenges. Through describing each study in detail, we demonstrate the applicability of different methods in a variety of domains in biomedical research, including genomics, biocuration and clinical research. Furthermore, we discuss and highlight the strengths and limitations of different crowdsourcing platforms. Finally, we identify important emerging trends, opportunities and remaining challenges for future crowdsourcing research in biomedicine.
authors.forename: Ritu
authors.initials: R
authors.lastname: Khare
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Good
authors.forename: Robert
authors.initials: R
authors.lastname: Leaman
authors.forename: Andrew I
authors.initials: AI
authors.lastname: Su
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Lu
mesh_terms: D019295:Computational Biology / Q000639:trends; D063045:Crowdsourcing / Q000639:trends*; D057225:Data Mining; D006801:Humans; D020407:Internet; D057386:Search Engine; D000068997:Smartphone; D061108:Social Media; D018910:Video Games
keywords: Amazon Mechanical Turk; big data mining; biomedicine; community challenges; crowdsourcing; games