Papers

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A
Danysz, K., Cicirello, S., Mingle, E., Assuncao, B., Tetarenko, N., Mockute, R., et al.. (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
Musib, M., Wang, F., Tarselli, M. A., Yoho, R., Yu, K. - H., Andrés, R. Medina, et al.. (2017). Artificial intelligence in research. Science, 357(6346), 28 - 30. https://doi.org/10.1126/science.357.6346.28
C
Poon, S. S., Thomas, R. C., Aragon, C. R., & Lee, B.. (2008). Context-linked virtual assistants for distributed teams (B. Begole & McDonald, D. W., Trans.). In the ACM 2008 conferenceProceedings of the ACM 2008 conference on Computer supported cooperative work - CSCW '08 (361). https://doi.org/10.1145/1460563.1460623
Poon, S. S., Thomas, R. C., Aragon, C., & Lee, B.. (2008). Context-linked virtual assistants for distributed teams: an astrophysics case study. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (361–370).
D
Møller, N. L. Holten, Bjørn, P., Villumsen, J. Christoffe, Hancock, T. C. Hansen, Aritake, T., & Tani, S.. (2017). Data tracking in search of workflows (C. P. Lee, Poltrock, S., Barkhuus, L., Borges, M., & Kellogg, W., Trans.). In the 2017 ACM ConferenceProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW '17 (2153 - 2165). https://doi.org/10.1145/2998181.2998296
Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N. N. C., et al.. (2017). A deep learning approach for quantifying tumor extent. Scientific Reports, 7(1). https://doi.org/10.1038/srep46450
Goldfarb, A., & Tucker, C.. (2019). Digital Economics. Journal Of Economic Literature, 57(1), 3 - 43. https://doi.org/10.1257/jel.20171452

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