Papers
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. (2018). Lecture Notes in Computer ScienceDesign, User Experience, and Usability: Theory and PracticeComparing Human Against Computer Generated Designs: New Possibilities for Design Activity Within Agile Projects (Vol. 10918, pp. 693 - 710; ). In (Vol. 10918, pp. 693 - 710). https://doi.org/10.1007/978-3-319-91797-9_48
. (2018). Making Sure We Don’t Forget the Basics When Using Machine Learning. Jnci: Journal Of The National Cancer Institute, 111(6), 529 - 530. https://doi.org/10.1093/jnci/djy179
. (2018). Occupational Classifications With A Machine Learning Approach. In IZA DP No. 11738. Retrieved de https://www.iza.org/publications/dp/11738/occupational-classifications-a-machine-learning-approach
. (2018). Researchably's AI parsers medical research for pharmaceutical companies. Retrieved de venturebeat.com website: https://venturebeat.com/2018/10/02/researchablys-ai-parses-medical-research-for-pharmaceutical-companies/
. (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
. (2018). Skill shift automation and the future of the workforce. In McKinsey Global Institute. Retrieved de https://www.mckinsey.com/featured-insights/future-of-work/skill-shift-automation-and-the-future-of-the-workforce
. (2018). Sustainable skills for the world of work in the digital age. Systems Research And Behavioral Science, 35(4), 399 - 405. https://doi.org/10.1002/sres.v35.410.1002/sres.2540
. (2018). Understanding tasks, automation, and the national health service (Vol. 10766, pp. 544 - 549; ). In (Vol. 10766, pp. 544 - 549). https://doi.org/10.1007/978-3-319-78105-1_60
. (2018). Understanding tasks, automation, and the national health service (Vol. 10766, pp. 544 - 549; ). In (Vol. 10766, pp. 544 - 549). https://doi.org/10.1007/978-3-319-78105-1_60
. (2018). Work that Enables Care: Understanding Tasks, Automation, and the National Health Service. In iConference. Presented at the iConference. https://doi.org/10.1007/978-3-319-78105-1_60
. (2019). Algorithms at War: The Promise, Peril, and Limits of Artificial Intelligence. International Studies Review. https://doi.org/10.1093/isr/viz025
. (2019). Is an army of robots marching on Chinese jobs?. Iza – Institute Of Labor Economics, (IZA DP No. 12281). Retrieved de https://www.iza.org/publications/dp/12281/is-an-army-of-robots-marching-on-chinese-jobs
. (2019). Artificial Intelligence and the Future of the Drug Safety Professional. Drug Safety, 42(4), 491 - 497. https://doi.org/10.1007/s40264-018-0746-z
. (2019). Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark. European Journal Of Cancer, 111, 30 - 37. https://doi.org/10.1016/j.ejca.2018.12.016
. (2019). Consider the human work experience when integrating robotics in the workplace. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (75 - 84). https://doi.org/10.1109/HRI.2019.8673139
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (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