In addition to the references below, you can use the following search strategy in Google Scholar to identify more literature on these and other tools (this is not a comprehensive search):
Gartlehner, G., Wagner, G., Lux, L., Affengruber, L., Dobrescu, A., Kaminski-Hartenthaler, A., & Viswanathan, M. (2019). Assessing the Accuracy of Machine-Assisted Abstract Screening with DistillerAI: A User Study. Systematic Reviews, 8(1), 277. doi:10.1186/s13643-019-1221-3
Gates, A., Guitard, S., Pillay, J., Elliott, S. A., Dyson, M. P., Newton, A. S., & Hartling, L. (2019, Nov 15). Performance and Usability of Machine Learning for Screening in Systematic Reviews: A Comparative Evaluation of Three Tools. Systematic Reviews, 8(1), 278. doi:10.1186/s13643-019-1222-2
Hamel, C., Hersi, M., Kelly, S. E., Tricco, A. C., Straus, S., Wells, G., Pham, B., & Hutton, B. (2021, Dec 20). Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses. BMC Medical Research Methodology, 21(1), 285. doi:10.1186/s12874-021-01451-2
O'Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., & Ananiadou, S. (2015). Using Text Mining for Study Identification in Systematic Reviews: A Systematic Review of Current Approaches. Systematic Reviews, 4, 5. doi:10.1186/2046-4053-4-5
Olorisade, B. K., Quincey, E. d., Brereton, P., & Andras, P. (2016). A Critical Analysis of Studies That Address the Use of Text Mining for Citation Screening in Systematic Reviews. Paper presented at the Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, Limerick, Ireland. doi:10.1145/2915970.2915982
Przybyla, P., Brockmeier, A. J., Kontonatsios, G., Le Pogam, M. A., McNaught, J., von Elm, E., . . . Ananiadou, S. (2018). Prioritising References for Systematic Reviews with Robotanalyst: A User Study. Research Synthesis Methods. doi:10.1002/jrsm.1311
van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Hindriks, S., Tummers, L., & Oberski, D. L. (2021). An Open Source Machine Learning Framework for Efficient and Transparent Systematic Reviews. Nature Machine Intelligence, 3(2), 125-133. doi:10.1038/s42256-020-00287-7
Wang, Z., Nayfeh, T., Tetzlaff, J., O’Blenis, P., & Murad, M. H. (2020). Error rates of human reviewers during abstract screening in systematic reviews. PLoS One, 15(1), e0227742. doi:10.1371/journal.pone.0227742
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