Comparing Artificial Intelligence and manual methods in systematic review processes: protocol for a systematic review

-д хадгалсан:
Номзүйн дэлгэрэнгүй
-д хэвлэсэн:Journal of Clinical Epidemiology vol. 181 (May 2025)
Үндсэн зохиолч: Pang, Xuenan
Бусад зохиолчид: Saif-Ur-Rahman, K M, Berhane, Sarah, Yao, Xiaomei, Kothari, Kavita, Petek Eylül Taneri, Thomas, James, Devane, Declan
Хэвлэсэн:
Elsevier Limited
Нөхцлүүд:
Онлайн хандалт:Citation/Abstract
Full Text
Full Text - PDF
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024 7 |a 10.1016/j.jclinepi.2025.111738  |2 doi 
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100 1 |a Pang, Xuenan  |u Centre for Health Research Methodology, School of Nursing and Midwifery, University of Galway, Galway, Ireland; Evidence Synthesis Ireland and Cochrane Ireland, University of Galway, Galway, Ireland 
245 1 |a Comparing Artificial Intelligence and manual methods in systematic review processes: protocol for a systematic review 
260 |b Elsevier Limited  |c May 2025 
513 |a Evidence Based Healthcare Journal Article 
520 3 |a Objectives This systematic review aims to evaluate the effectiveness of automated methods using artificial intelligence (AI) in conducting systematic reviews, with a focus on both performance and resource utilization compared to human reviewers. Study Design and Setting This systematic review and meta-analysis protocol follows the Cochrane Methodology protocol and review guidance. We searched five bibliographic databases to identify potential studies published in English from 2005. Two independent reviewers will screen the titles and abstracts, followed by a full-text review of the included articles. Any discrepancies will be resolved through discussion and, if necessary, referral to a third reviewer. The risk of bias (RoB) in included studies will be assessed at the outcome level using the revised Cochrane risk-of-bias tool for randomized trials and the RoB In Non-randomized Studies - of Interventions for non-randomized studies. Where appropriate, we plan to conduct meta-analysis using random-effects models to obtain pooled estimates. We will explore the sources of heterogeneity and conduct sensitivity analyses based on prespecified characteristics. Where meta-analysis is not feasible, a narrative synthesis will be performed. Results We will present the results of this review, focusing on performance and resource utilization metrics. Conclusion This systematic review will evaluate the effectiveness of automated methods, especially AI tools in systematic reviews, aiming to synthesize current evidence on their performance, resource utilization, and impact on review quality. The findings will inform evidence-based recommendations for systematic review authors and developers on implementing automation tools to optimize review efficiency while maintaining methodological rigor. In addition, we will identify key research gaps to guide future development of AI-assisted systematic review methods. Plain Language Summary A systematic review is a thorough and organized summary of all relevant studies on a specific topic. These reviews are important for gathering evidence to guide health care decisions, but they often take a lot of time and effort. Recently, tools using artificial intelligence (AI) have been developed to speed up this process. We will conduct a systematic review to see how well these AI tools perform compared to human reviewers. We will examine studies from 2005 that have used AI to conduct systematic reviews. We will assess how well AI tools find the right information, how much time and work they save, and how easy and reliable they are for users. This study aims to help researchers choose the best AI tools to make systematic reviews faster and more efficient without losing quality. 
653 |a Artificial intelligence 
653 |a Bias 
653 |a Collaboration 
653 |a Sensitivity analysis 
653 |a Data mining 
653 |a Open source software 
653 |a Automation 
653 |a Workloads 
653 |a Heterogeneity 
653 |a Efficiency 
653 |a Computers 
653 |a Query expansion 
653 |a Human performance 
653 |a Effectiveness 
653 |a Natural language processing 
653 |a Algorithms 
653 |a Meta-analysis 
653 |a Resource utilization 
653 |a Systematic review 
653 |a Social 
700 1 |a Saif-Ur-Rahman, K M  |u Centre for Health Research Methodology, School of Nursing and Midwifery, University of Galway, Galway, Ireland; Evidence Synthesis Ireland and Cochrane Ireland, University of Galway, Galway, Ireland 
700 1 |a Berhane, Sarah  |u Department of Applied Health Science, School of Health Sciences, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK 
700 1 |a Yao, Xiaomei  |u Department of Oncology, McMaster University, Hamilton, Ontario, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada 
700 1 |a Kothari, Kavita  |u Consultant, Evidence Synthesis Ireland and Cochrane Ireland, University of Galway, Galway, Ireland 
700 1 |a Petek Eylül Taneri  |u Centre for Health Research Methodology, School of Nursing and Midwifery, University of Galway, Galway, Ireland; Evidence Synthesis Ireland and Cochrane Ireland, University of Galway, Galway, Ireland 
700 1 |a Thomas, James  |u EPPI-Centre, UCL Social Research Institute, University College London, London, UK 
700 1 |a Devane, Declan  |u Centre for Health Research Methodology, School of Nursing and Midwifery, University of Galway, Galway, Ireland; Evidence Synthesis Ireland and Cochrane Ireland, University of Galway, Galway, Ireland; HRB-Trials Methodology Research Network, University of Galway, Galway, Ireland 
773 0 |t Journal of Clinical Epidemiology  |g vol. 181 (May 2025) 
786 0 |d ProQuest  |t Healthcare Administration Database 
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