References

ACRL Research Planning and Review Committee. (2020). 2020 top trends in academic libraries. Library Faculty Presentations & Publications, 21(6), 270. https://doi.org/10.5860/crln.81.6.270

Arno, A., Thomas, J., Wallace, B., Marshall, I. J., McKenzie, J. E., & Elliott, J. H. (2022). Accuracy and efficiency of machine learning-assisted risk-of-bias assessments in “real-world” systematic reviews a noninferiority randomized controlled trial. Annals of Internal Medicine, 175(7), 1001–1009. https://doi.org/10.7326/m22-0092

Aromataris, E., & Munn, Z. (2020). JBI Manual for Evidence Synthesis. JBI. https://doi.org/10.46658/JBIMES-20-01

Asemi, A., Ko, A., & Nowkarizi, M. (2021). Intelligent libraries: a review on expert systems, artificial intelligence, and robot. Library Hi Tech, 39(2), 412–434. https://doi.org/10.1108/LHT-02-2020-0038

Azzopardi, L. (2021). Cognitive Biases in Search: A Review and Reflection of Cognitive Biases in Information Retrieval. SIGIR’ 21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 27–37. https://doi.org/10.1145/3406522.3446023

Bakke, A. (2020). Everyday googling: Results of an observational study and applications for teaching algorithmic literacy. Computers and Composition, 57, 1–16. https://doi.org/10.1016/j.compcom.2020.102577

Barroga, E. (2020). Innovative strategies for peer review. Journal of Korean Medical Science, 35(20), Article e138. https://doi.org/10.3346/jkms.2020.35.e138

Beller, E., Clark, J., Tsafnat, G., Adams, C., Diehl, H., Lund, H., Ouzzani, M., Thayer, K., Thomas, J., Turner, T., Xia, J., Robinson, K., & Glasziou, P. (2018). Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Systematic Reviews, 7, Article 77. https://doi.org/10.1186/s13643-018-0740-7

Bethard, S., Ghosh, S., Martin, J., & Sumner, T. (2009). Topic model methods for automatically identifying out-of-scope resources. JCDL’ 09 International Conference on Digital Libraries (2009), 19–28.

Clark, J., McFarlane, C., Cleo, G., Ishikawa Ramos, C., & Marshall, S. (2021). The impact of systematic review automation tools on methodological quality and time taken to complete systematic review tasks: Case study. JMIR Medical Education, 7(2), Article e24418. https://doi.org/10.2196/24418

Ewing, K., & Hauptman, R. (1995). Is traditional reference service obsolete? The Journal of Academic Librarianship, 21(1), 3–6. https://doi.org/10.1016/0099-1333(95)90144-2

Gasparini, A., & Kautonen, H. (2022). Understanding artificial intelligence in research libraries – Extensive literature review. LIBER Quarterly, 32(1), 1–36. https://doi.org/10.53377/lq.10934

Gozzo, M., Woldendorp, M. K., & De Rooij, A. (2022). Creative collaboration with the “brain” of a search engine: Effects on cognitive stimulation and evaluation apprehension. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 209–223. https://doi.org/10.1007/978-3-030-95531-1_15

Henry, G. (2019). Research librarians as guides and navigators for AI policies at Universities. Research Library Issues, 299, 47–65. https://doi.org/10.29242/rli.299.4

Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2022). Cochrane handbook for systematic reviews of interventions version 6.3. The Cochrane Collaboration. https://training.cochrane.org/handbook

Hofman-Apitius, M., Younesi, E., & Kasam, V. (2009). Direct use of information extraction from scientific text for modeling and simulation in the life sciences. Library Hi Tech, 27(4), 505–519. https://doi.org/10.1108/07378830911007637

Institute for Work & Health (2008, April). What researchers mean by…Grey literature. https://www.iwh.on.ca/what-researchers-mean-by/grey-literature

Jakeway, E., Algee, L., Allen, L., Ferriter, M., Mears, J., Potter, A., & Zwaard, K. (2020). Machine learning + libraries summit event summary. Library of Congress. https://labs.loc.gov/static/labs/meta/ML-Event-Summary-Final-2020-02-13.pdf

Johnsen, S. S., Lyngsfeldt, J., Vils, A., & Wildgaard, L. (2022). Exploring Iris.ai and Yewno.discover with a Hackathon and expert quality assessment [Report]. Zenodo. https://doi.org/10.5281/ZENODO.6221505

Kennedy, M. L. (2019). What do artificial intelligence (AI) and ethics of AI mean in the context of research libraries? Research Library Issues, 299, 3–13. https://doi.org/10.29242/rli.299.1

Khalil, H., Ameen, D., & Zarnegar, A. (2022). Tools to support the automation of systematic reviews: a scoping review. Journal of Clinical Epidemiology, 144, 22–42. https://doi.org/10.1016/j.jclinepi.2021.12.005

Kjær, L., Tang, H., & Richter, N. H. (2020). Finnerne satser stort på kunstig intelligens. REVY, 43(1), 18–21. https://doi.org/10.22439/revy.v43i1.5939

Kricka, L. J., Polevikov, S., Park, J. Y., Fortina, P., Bernardini, S., Satchkov, D., Kolesov, V., & Grishkov, M. (2020). Artificial intelligence-powered search tools and resources in the fight against COVID-19. EJIFCC, 31(2), 106–116.

Lefebvre, C., Glanville, J., Briscoe, S., Featherstone, R., Littlewood, A., Marshall, C., Metzendorf, M.-I., Noel-Storr, A., Paynter, R., Rader, T., Thomas, J., & Wieland, L. (2022). Technical Supplement to Chapter 4: Searching for and selecting studies. In J. P. T. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M. J. Page, & V. A. Welch (Eds.), Cochrane handbook for systematic reviews of interventions Version 6.3 (updated February 2022) (2nd ed.). The Cochrane Collaboration. https://training.cochrane.org/handbook/current/chapter-04

Lyngsfeldt, J. K., Wildgaard, L., Møller, A. V., & Johnsen, S. S. (2022). Artificial Intelligence og litteratursøgning i biblioteksregi. REVY, 45(2), 7–9. https://doi.org/10.22439/revy.v45i2.6629

Marshall, I. J., & Wallace, B. C. (2019). Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Systematic Reviews, 8(1), 163. https://doi.org/10.1186/s13643-019-1074-9

NHS Centre for Reviews and Dissemination (2009). Systematic reviews: CRD’s guidance for undertaking reviews in health care. Centre for Reviews and Dissemination. https://www.york.ac.uk/media/crd/Systematic_Reviews.pdf

Nielsen, J. (1993). Usability engineering. Academic Press.

Nolin, J. M. (2013). The special librarian and personalized meta-services: Strategies for reconnecting librarians and researchers. Library Review (Glasgow), 62(8–9), 508–524. https://doi.org/10.1108/LR-02-2013-0015

Orgeolet, L., Foulquier, N., Misery, L., Redou, P., Pers, J.-O., Devauchelle-Pensec, V., & Saraux, A. (2020). Can artificial intelligence replace manual search for systematic literature? Review on cutaneous manifestations in primary Sjögren’s syndrome. Rheumatology, 59(4), 811–819. https://doi.org/10.1093/rheumatology/kez370

Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic Reviews, 5(1), Article 210. https://doi.org/10.1186/s13643-016-0384-4

Polonioli, A. (2020). In search of better science: on the epistemic costs of systematic reviews and the need for a pluralistic stance to literature search. Scientometrics, 122(2), 1267–1274. https://doi.org/10.1007/s11192-019-03333-3

Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., & Koffel, J. B. (2021). PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Systematic Reviews, 10(1), 39. https://doi.org/10.1186/s13643-020-01542-z

Scientific Hackathon (2023, 15 February 2023). In Wikiversity. https://en.wikiversity.org/wiki/Scientific_Hackathon

Schoeb, D., Suarez-Ibarrola, R., Hein, S., Dressler, F. F., Adams, F., Schlager, D., & Miernik, A. (2020). Use of artificial intelligence for medical literature search: Randomized controlled trial using the hackathon format. Interactive Journal of Medical Research, 9(1), Article e16606. https://doi.org/10.2196/16606

Thomas, J., Brunton, J., & Graziosi, S. (2010). EPPI-Reviewer 4.0: Software for research synthesis. EPPI-Centre Software [Computer software]. EPPI-Centre.

van den Haak, M., De Jong, M., & Schellens, P. J. (2003). Retrospective vs. concurrent Think-aloud protocols: testing the usability of an online library catalogue. Behaviour & Information Technology, 22(5), 339–351. https://doi.org/10.1080/0044929031000

Wallace, B. C., Small, K., Brodley, C. E., Lau, J., & Trikalinos, T. A. (2012). Deploying an interactive machine learning system in an evidence-based practice center: Abstrackr. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, USA, 819–824. https://doi.org/10.1145/2110363.2110464

Wildgaard, L., Johnsen, S. S., & Kiersgaard, J. (2020). Delivery 1: Selection of AI software [Project deliverable]. Zenodo. https://doi.org/10.5281/ZENODO.4279009

Wildgaard, L., Møller, A. V., Kiersgaard, J., & Johnsen, S. S. (2021). Delivery 2: Exploring Iris.ai and Yewno with Think-Aloud tests – a mid-term perspective [Project deliverable]. Zenodo. https://doi.org/10.5281/ZENODO.5350927

Wu, R., Stauber, V., Botev, V., Elosua, J., Brede, A., Ritola, M., & Marinov, K. (2018). Scithon™ – An evaluation framework for assessing research productivity tools. In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, T. Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). http://lrec-conf.org/workshops/lrec2018/W24/pdf/7_W24.pdf

Zhang, Y., Liang, S., Feng, Y., Wang, Q., Sun, F., Chen, S., Yang, Y., He, X., Zhu, H., & Pan, H. (2022). Automation of literature screening using machine learning in medical evidence synthesis: a diagnostic test accuracy systematic review protocol. Systematic Reviews, 11, Article 11. https://doi.org/10.1186/s13643-021-01881-5