Artificial Intelligence (AI) Readiness to Support Evidence Synthesis by Workflow: Findings From a Review of Reviews
Zijing Wei, Luyanda Ngongoma, Jose Cols, Arina L. Bogdan, Ariel Lin, Claire Zhang, Yue Su, Nuno de Jesus Ximenes, Chloe Zhu, Yoav Ackerman, Heather L. Bullock, Juhua Hu, Yanfang SuBackground
Evidence synthesis is crucial for informing evidence-based practice across various fields. However, the traditional methodology is resource-intensive, and its findings can be outdated before publication. There is a growing trend toward integrating automation and artificial intelligence (AI) approaches into evidence synthesis to enhance efficiency, but standardized adoption is still pending.
Objective
The goal of this study is to identify peer-reviewed evidence documenting AI readiness for evidence synthesis.
Methods
We searched MEDLINE, Embase, and Global Index Medicus in May 2025 to identify review articles that evaluated evidence synthesis tools. Relevant study reviews and tool reviews published in English between January 2020 and May 2025 were included in our review of reviews. Tool features and performance metrics were extracted according to stages of the evidence synthesis workflow, including search, screening, appraisal, extraction, and synthesis.
Results
We included 21 studies in our review of reviews and identified 46 evidence synthesis tools. Nine tools supported all five stages of the evidence synthesis workflow, among which DistillerSR covered the most workflow-supporting features (19 out of 21). Ten of the identified tools reported sensitivity rates for AI-powered title/abstract screening, all of which achieved
Conclusion
This review found peer-reviewed evidence supporting AI readiness for human-supervised automation of title/abstract screening. However, evidence documenting AI readiness for other evidence synthesis tasks remains limited. DistillerSR and EPPI-Reviewer demonstrated the broadest feature support and strong evidence for AI-powered title/abstract screening. Our study highlights the potential of AI to improve efficiency while maintaining high sensitivity in the screening stage. AI-powered screening may serve as a critical first step toward scaling rapid reviews into living evidence syntheses.