@techreport{FischerArnoldYesilbas2023,
  author    = {Fischer, Josephine and Arnold, Stefan and Yesilbas, Dilara},
  title     = {Crowd-Powered Medical Diagnosis : The Potential of Crowdsourcing for Patients with Rare Diseases},
  series    = {NextGen Scientific Review},
  number    = {1},
  issn      = {2940-0929},
  doi       = {10.48446/opus-13720},
  pages     = {10 -- 18},
  year      = {2023},
  abstract  = {With the recent rise in medical crowdsourcing platforms, patients with chronic illnesses increasingly broadcast their medical records to obtain an explanation for their complex health conditions. By providing access to a vast pool of diverse medical knowledge, crowdsourcing platforms have the potential to change the way patients receive a medical diagnosis. We developed a conceptual model that details a set of variables. To further the understanding of crowdsourcing as an emerging phenomenon in health care, we provide a contextualization of the various factors that drive participants to exert effort. For this purpose, we used CrowdMed.com as a platform from which we gathered and examined a unique dataset that involves tasks of diagnosing rare medical conditions. By promoting crowdsourcing as a robust and non-discriminatory alternative to seeking help from traditional physicians, we contribute to the acceptance and adoption of crowdsourcing services in health economics.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}