Published on in Vol 7, No 2 (2021): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24418, first published .
The Impact of Systematic Review Automation Tools on Methodological Quality and Time Taken to Complete Systematic Review Tasks: Case Study

The Impact of Systematic Review Automation Tools on Methodological Quality and Time Taken to Complete Systematic Review Tasks: Case Study

The Impact of Systematic Review Automation Tools on Methodological Quality and Time Taken to Complete Systematic Review Tasks: Case Study

Journals

  1. Christopoulou S. Machine Learning Tools and Platforms in Clinical Trial Outputs to Support Evidence-Based Health Informatics: A Rapid Review of the Literature. BioMedInformatics 2022;2(3):511 View
  2. Pourreza M, Ensan F. Towards semantic-driven boolean query formalization for biomedical systematic literature reviews. International Journal of Medical Informatics 2023;170:104928 View
  3. Khalil H, Ameen D, Zarnegar A. Tools to support the automation of systematic reviews: a scoping review. Journal of Clinical Epidemiology 2022;144:22 View
  4. Grbin L, Nichols P, Russell F, Fuller-Tyszkiewicz M, Olsson C. The Development of a Living Knowledge System and Implications for Future Systematic Searching. Journal of the Australian Library and Information Association 2022;71(3):275 View
  5. Muller A, Berg R, Meneses-Echavez J, Ames H, Borge T, Jardim P, Cooper C, Rose C. The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study. Systematic Reviews 2023;12(1) View
  6. Chaboyer W, Coyer F, Harbeck E, Thalib L, Latimer S, Wan C, Tobiano G, Griffin B, Campbell J, Walker R, Carlini J, Lockwood I, Clark J, Gillespie B. Oedema as a predictor of the incidence of new pressure injuries in adults in any care setting: A systematic review and meta-analysis. International Journal of Nursing Studies 2022;128:104189 View
  7. Cowie K, Rahmatullah A, Hardy N, Holub K, Kallmes K. Web-Based Software Tools for Systematic Literature Review in Medicine: Systematic Search and Feature Analysis. JMIR Medical Informatics 2022;10(5):e33219 View
  8. Sharp R, Guenther D, Farrer M. Experimental procedures for flow cytometry of wild-type mouse brain: a systematic review. Frontiers in Immunology 2023;14 View
  9. Roco-Videla Á, Caviedes-Olmos M, Aguilera-Eguía R, Olguín-Barraza M. Artificial intelligence and its implication in the development of systematized reviews. Uses and limitations. Nutrición Hospitalaria 2023 View
  10. Orel E, Ciglenecki I, Thiabaud A, Temerev A, Calmy A, Keiser O, Merzouki A. An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study. Journal of Medical Internet Research 2023;25:e39736 View
  11. Guo E, Gupta M, Deng J, Park Y, Paget M, Naugler C. Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study. Journal of Medical Internet Research 2024;26:e48996 View
  12. Affengruber L, Nussbaumer-Streit B, Hamel C, Van der Maten M, Thomas J, Mavergames C, Spijker R, Gartlehner G. Rapid review methods series: Guidance on the use of supportive software. BMJ Evidence-Based Medicine 2024;29(4):264 View
  13. Plummer K, Adina J, Mitchell A, Lee-Archer P, Clark J, Keyser J, Kotzur C, Qayum A, Griffin B. Digital health interventions for postoperative recovery in children: a systematic review. British Journal of Anaesthesia 2024;132(5):886 View
  14. Chaboyer W, Latimer S, Priyadarshani U, Harbeck E, Patton D, Sim J, Moore Z, Deakin J, Carlini J, Lovegrove J, Jahandideh S, Gillespie B. The effect of pressure injury prevention care bundles on pressure injuries in hospital patients: A complex intervention systematic review and meta-analysis. International Journal of Nursing Studies 2024;155:104768 View
  15. Dennstädt F, Zink J, Putora P, Hastings J, Cihoric N. Title and abstract screening for literature reviews using large language models: an exploratory study in the biomedical domain. Systematic Reviews 2024;13(1) View
  16. Tóth B, Berek L, Gulácsi L, Péntek M, Zrubka Z. Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed. Systematic Reviews 2024;13(1) View
  17. Askin N, Okoli G. Deduplication methods for literature citations in systematic evidence reviews: practical insights to guide decision-making. Health Services and Outcomes Research Methodology 2025;25(2):214 View
  18. Affengruber L, van der Maten M, Spiero I, Nussbaumer-Streit B, Mahmić-Kaknjo M, Ellen M, Goossen K, Kantorova L, Hooft L, Riva N, Poulentzas G, Lalagkas P, Silva A, Sassano M, Sfetcu R, Marqués M, Friessova T, Baladia E, Pezzullo A, Martinez P, Gartlehner G, Spijker R. An exploration of available methods and tools to improve the efficiency of systematic review production: a scoping review. BMC Medical Research Methodology 2024;24(1) View
  19. Tomczyk P, Brüggemann P, Vrontis D. AI meets academia: transforming systematic literature reviews. EuroMed Journal of Business 2024 View
  20. Bailey R, MacFarlane A, Field M, Tagkopoulos I, Baranzini S, Edwards K, Rose C, Schork N, Singhal A, Wallace B, Fisher K, Markakis K, Stover P, Bovell-Benjamin A. Artificial intelligence in food and nutrition evidence: The challenges and opportunities. PNAS Nexus 2024;3(12) View
  21. Abogunrin S, Muir J, Zerbini C, Sarri G. How much can we save by applying artificial intelligence in evidence synthesis? Results from a pragmatic review to quantify workload efficiencies and cost savings. Frontiers in Pharmacology 2025;16 View
  22. Barrett-Catton E, Jones E, Carlson R. Effect of Citation Numbers and Team Members on the Possibility of and Time Needed to Complete Screening for Systematic and Scoping Reviews. Medical Reference Services Quarterly 2025:1 View
  23. Qin X, Yao M, Luo X, Liu J, Ma Y, Liu Y, Li H, Deng K, Zou K, Li L, Sun X. Machine learning for identifying randomised controlled trials when conducting systematic reviews: Development and evaluation of its impact on practice. Research Synthesis Methods 2025;16(2):350 View
  24. Sujau M, Wada M, Vallée E, Hillis N, Sušnjak T. Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation. Machine Learning and Knowledge Extraction 2025;7(2):28 View
  25. Madanchian M, Taherdoost H. The impact of artificial intelligence on research efficiency. Results in Engineering 2025;26:104743 View
  26. Zhou F, Parrish R, Afzal M, Saha A, Haynes R, Iorio A, Lokker C. Benchmarking domain-specific pretrained language models to identify the best model for methodological rigor in clinical studies. Journal of Biomedical Informatics 2025;166:104825 View
  27. Boyle A, Huo B, Sylla P, Calabrese E, Kumar S, Slater B, Walsh D, Vosburg R. Large language model-generated clinical practice guideline for appendicitis. Surgical Endoscopy 2025 View
  28. Ramchandani R, Guo E, Rakab E, Rathod J, Strain J, Klement W, Shorr R, Williams E, Jones D, Gilbert S. Validation of automated paper screening for esophagectomy systematic review using large language models. PeerJ Computer Science 2025;11:e2822 View
  29. Shapovalov Y, Shapovalov V, Rudakova T, Rybalko Y. Automation of document preparation workflow in scientific activities: practical approaches and using of ontologies. Scientific Notes of Junior Academy of Sciences of Ukraine 2025;(1(32)):93 View
  30. Rose C, Meneses‐Echavez J, Muller A, Berg R, Borge T, Jardim P, Cooper C. Artificial Intelligence and Machine Learning to Improve Evidence Synthesis Production Efficiency: An Observational Study of Resource Use and Time‐to‐Completion. Cochrane Evidence Synthesis and Methods 2025;3(3) View

Books/Policy Documents

  1. Đukić M, Škembarević M, Jejić O, Luković I. New Trends in Database and Information Systems. View
  2. . How to Read a Paper. View

Conference Proceedings

  1. Sandner E, Hu B, Simiceanu A, Fontana L, Jakovljevic I, Henriques A, Wagner A, Gütl C. 2024 2nd International Conference on Foundation and Large Language Models (FLLM). Screening Automation for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane’s Sensitivity Requirement View