Crowdsourcing in the form of human-based electronic services provides a powerful way of outsourcing so called human intelligence tasks (HITs) to a large workforce of people over the Internet. Because of the limited control over that workforce, it is challenging to ensure the quality of the work results. Several approaches have been proposed that can be applied to specific types of HITs. However, it is difficult to identify a suitable quality management approach for any given type of HIT. This paper aims to provide a first sketch of a decision matrix.
Less incidents in heart surgeries, less time-to-intervention in case of a stroke, lower costs due to less claim denials for hospitals, less deaths due to an earlier detection of breast cancer—these are real-world examples of AI being the catalyst of the next quantum leap in healthcare. Researchers, entrepreneurs, and tech incumbents are active within all subfields of healthcare and proofing the value of data-driven and AI-based initiatives. This paper aims at giving an overview of the broad variety of such engagements within six major fields in healthcare. Each of the six sections itself provides an overview of the most important AI applications within that field and a zoom-in into exemplary research work and already implemented industry solutions. A broad adoption of AI applications within healthcare institutions has not yet occurred, due to some current limitations in data quality and governance, industry standards, and ethical issues.
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