Papers
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. (2020).
AI-Based Digital Assistants. Business & Information Systems Engineering, 61(4), 535 - 544. https://doi.org/10.1007/s12599-019-00600-8
. (2019). Artificial Intelligence and Deep Learning: The Future of Medicine and Medical Practice. Journal Of The Association Of Physicians Of India ■ Vol. 67 . Retrieved de https://www.semanticscholar.org/paper/Artificial-Intelligence-and-Deep-Learning%3A-The-of-Sanal-Paul/795dc72decfb12389bbf7076d82fef70421b9933
. (2019). Artificial intelligence in workplaces and how it will affect employment in Latvia. Environment. Technology. Resources. Proceedings Of The International Scientific And Practical Conference, 2, 154. https://doi.org/10.17770/etr2019vol2.4151
. (2019). Beyond Dyadic Interactions: Considering Chatbots as Community Members ( ). In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19 (1-13). https://doi.org/10.1145/3290605
. (2019). Blame the boss, not the robot. New Labor Forum, 28(2), 66 - 69. https://doi.org/10.1177/1095796019837947
. (2019). Bots Coordinating Work in Open Source Software Projects. Computer, 52(9), 52 - 60. https://doi.org/10.1109/MC.2018.2885970
. (2019). A chatbot for supporting teams in the empathy map method ( ). In Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.029
. (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). 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). 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). 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). 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).