Papers
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. (2018). Robots worldwide: The impact of automation on employment and trade. In International Labour Office. Retrieved de https://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_648063.pdf
. (2017). An empirical analysis of the impacts of robotization on employment in the Norwegian manufacturing industry. In Administration, Economics. Retrieved de https://www.semanticscholar.org/paper/Will-robots-replace-us-%3A-an-Empirical-analysis-of-Gr%C3%B8ndahl-Eriksen/e4e22bf2498e42d6308d81cc7483b5a0b8ba48e7
. (2018). The economics of artificial intelligence: Implications for the future of work. In International Labour Office. Retrieved de https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_647306.pdf
. (2017). Understanding human-machine networks. Acm Computing Surveys, 50(1), 1 - 35. https://doi.org/10.1145/3039868
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The Tqm Journal, ahead-of-p. https://doi.org/10.1108/TQM-12-2019-0303
. (2014). Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter. Computers In Human Behavior, 33, 372 - 376. https://doi.org/10.1016/j.chb.2013.08.013
. (2014). Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter. Computers In Human Behavior, 33, 372 - 376. https://doi.org/10.1016/j.chb.2013.08.013
. (2018). Technology-induced bias in the theory of evidence-based medicine. Journal Of Evaluation In Clinical Practice, 24(5), 945 - 949. https://doi.org/10.1111/jep.12972
. (2019). The present and future of deep learning in radiology. European Journal Of Radiology, 114, 14 - 24. https://doi.org/10.1016/j.ejrad.2019.02.038
. (2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. New England Journal Of Medicine, 375(13), 1216 - 1219. https://doi.org/10.1056/NEJMp1606181
. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information And Management, 57, 103174. https://doi.org/10.1016/j.im.2019.103174
. (2020). Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation. Ai, 1, 180–191. https://doi.org/10.3390/ai1020011
. (2021). Hybrid intelligence in business networks. Electronic Markets. https://doi.org/10.1007/s12525-021-00481-4
Ebel2021_Article_HybridIntelligenceInBusinessNe.pdf (605.72 KB). (2019). Hybrid Intelligence. Business & Information Systems Engineering, 61(5), 637 - 643. https://doi.org/10.1007/s12599-019-00595-2
. (2016). Differences in perceptions of communication quality between a Twitterbot and human agent for information seeking and learning. Computers In Human Behavior, 65, 666 - 671. https://doi.org/10.1016/j.chb.2016.07.003
. (2016). Differences in perceptions of communication quality between a Twitterbot and human agent for information seeking and learning. Computers In Human Behavior, 65, 666 - 671. https://doi.org/10.1016/j.chb.2016.07.003
. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–118.
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001