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The limiting factors are mostly training datasets that are too small and contain heterogenous image data. Since these algorithms are usually only designed to detect (and at best locate) an injury, the potential help in "clinical decision-making" has been very limited up to now. Most articles highlight the feasibility and accuracy of artificial neural networks for a certain purpose, but lack to prove its practical usability. To change this, a broadly based project with a clear goal and vision of implementing AI in daily routine is necessary.By following the suggested framework for scoping reviews, we intended to give an informative overview and allow the reader to find a systematic approach to this very heterogenous topic. A systematic literature search of the MEDLINE/PubMed and Cochrane Collaboration libraries was conducted. The review was conducted according to the framework outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews. A narrative summary of the papers is presented to give an orienting overview of this rapidly evolving topic. In this new world of AI, our tasks will get automated, but people won’t; our jobs may change but they won’t get replaced.
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