Please note, this is not an exhaustive list. For further information, please reach out to the library.
Agency for Healthcare Research and Quality. (2012). Machine Learning Methods in Systematic Reviews: Identifying Quality Improvement Intervention Evaluations. Effective Health Care Program, Agency for Healthcare Research and Quality. https://www.ncbi.nlm.nih.gov/books/NBK109711/
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
Chue Hong, N. P., Allen, A., Gonzalez-Beltran, A., de Waard, A., Smith, A. M., Robinson, C., Jones, C., Bouquin, D., Katz, D. S., Kennedy, D., Ryder, G., Hausman, J., Hwang, L., Jones, M. B., Harrison, M., Crosas, M., Wu, M., Löwe, P., Haines, R., … Pollard, T. (2019). Software Citation Checklist for Authors. https://doi.org/10.5281/ZENODO.3479198
Cierco Jimenez, R., Lee, T., Rosillo, N., Cordova, R., Cree, I. A., Gonzalez, A., & Indave Ruiz, B. I. (2022). Machine learning computational tools to assist the performance of systematic reviews: A mapping review. BMC Medical Research Methodology, 22(1), 322. https://doi.org/10.1186/s12874-022-01805-4
Clark, J., Glasziou, P., Del Mar, C., Bannach-Brown, A., Stehlik, P., & Scott, A. M. (2020). A full systematic review was completed in 2 weeks using automation tools: A case study. Journal of Clinical Epidemiology, 121, 81–90. https://doi.org/10.1016/j.jclinepi.2020.01.008
Devane, D., Burke, N. N., Treweek, S., Clarke, M., Thomas, J., Booth, A., Tricco, A. C., & Saif-Ur-Rahman, K. M. (2022). Study within a review (SWAR). Journal of Evidence-based Medicine, 15(4), 328–332. https://doi.org/10.1111/jebm.12505
Gartlehner, G., Kahwati, L., Nussbaumer-Streit, B., Crotty, K., Hilscher, R., Kugley, S., Viswanathan, M., Thomas, I., Konet, A., Booth, G., & Chew, R. (2024). From promise to practice: Challenges and pitfalls in the evaluation of large language models for data extraction in evidence synthesis. BMJ Evidence-Based Medicine, bmjebm-2024-113199. https://doi.org/10.1136/bmjebm-2024-113199
Goldkuhle, M., Dimaki, M., Gartlehner, G., Monsef, I., Dahm, P., Glossmann, J.-P., Engert, A., Von Tresckow, B., & Skoetz, N. (2018). Nivolumab for adults with Hodgkin’s lymphoma (a rapid review using the software RobotReviewer). Cochrane Database of Systematic Reviews, 2018(7). https://doi.org/10.1002/14651858.CD012556.pub2
Katz, D. S., Chue Hong, N. P., Clark, T., Muench, A., Stall, S., Bouquin, D., Cannon, M., Edmunds, S., Faez, T., Feeney, P., Fenner, M., Friedman, M., Grenier, G., Harrison, M., Heber, J., Leary, A., MacCallum, C., Murray, H., Pastrana, E., … Yeston, J. (2021). Recognizing the value of software: A software citation guide. F1000Research, 9, 1257. https://doi.org/10.12688/f1000research.26932.2
Malhotra, S. K., Saran, A., Singh, S., Mantri, S., Gupta, N., Bhandari, R., White, H., Puskur, R., Young, S., Waddington, H., & Masset, E. (2023). PROTOCOL: Value chain interventions for improving women’s economic empowerment: A mixed‐method systematic review. Campbell Systematic Reviews, 19(3), e1331. https://doi.org/10.1002/cl2.1331
Marshall, I. J., Kuiper, J., & Wallace, B. C. (2016). RobotReviewer: Evaluation of a system for automatically assessing bias in clinical trials. Journal of the American Medical Informatics Association: JAMIA, 23(1), 193–201. https://doi.org/10.1093/jamia/ocv044
Marshall, I. J., Trikalinos, T. A., Soboczenski, F., Yun, H. S., Kell, G., Marshall, R., & Wallace, B. C. (2023). In a pilot study, automated real-time systematic review updates were feasible, accurate, and work-saving. Journal of Clinical Epidemiology, 153, 26–33. https://doi.org/10.1016/j.jclinepi.2022.08.013
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
O’Connor, A. M., Tsafnat, G., Thomas, J., Glasziou, P., Gilbert, S. B., & Hutton, B. (2019). A question of trust: Can we build an evidence base to gain trust in systematic review automation technologies? Systematic Reviews, 8(1), 143. https://doi.org/10.1186/s13643-019-1062-0
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … McKenzie, J. E. (2021a). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. BMJ, n160. https://doi.org/10.1136/bmj.n160
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … McKenzie, J. E. (2021b). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. BMJ, n160. https://doi.org/10.1136/bmj.n160
Van Dijk, S. H. B., Brusse-Keizer, M. G. J., Bucsán, C. C., Van Der Palen, J., Doggen, C. J. M., & Lenferink, A. (2023). Artificial intelligence in systematic reviews: Promising when appropriately used. BMJ Open, 13(7), e072254. https://doi.org/10.1136/bmjopen-2023-072254
Wang, Q., Liao, J., Lapata, M., & Macleod, M. (2022). Risk of bias assessment in preclinical literature using natural language processing. Research Synthesis Methods, 13(3), 368–380. https://doi.org/10.1002/jrsm.1533