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How is Cochrane advancing responsible AI for evidence synthesis?
- Systematic reviews are built on the principles of rigour, transparency, and replicability. However, many current AI solutions don’t meet these principles. Cochrane says that "we are committed to addressing this challenge with an approach that is measured and responsible".
- Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.5 (updated August 2024). Cochrane, 2024.
How is Cochrane Integrating AI into Evidence Synthesis?
- Cochrane is implementing a range of automation solutions in its review processes; machine learning, for example, is used to identify randomized controlled trials (RCTs), and other technologies such as generative AI. CC is primed to use AI due to high-quality, structured data from systematic reviews and included studies; it can perform analysis in RevMan, which allows authors to insert live results directly into their reviews while they’re writing and updating them in CENTRAL.
- Cochrane plans to promote responsible AI combining automation and verification to help authors identify studies. CC plans to improve AI literacy across their organization.
- AI Methods Group <https://www.cochrane.org/about-us/news/new-ai-methods-group-spearhead-adoption-across-four-leading-evidence-synthesis-organizations> includes Cochrane, Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence. The groups aims to standardize responsible AI use across evidence synthesis organizations; focuses on methods research, tool validation, and fostering collaboration via the International Collaboration for Automation in Systematic Reviews (ICASR).
- Cochrane’s Evidence Synthesis and Methods journal encourages research on AI applications in evidence synthesis, such as search strategy development, screening, and risk of bias assessment. Recent studies highlight modest adoption of traditional AI tools but promising advancements in language editing and data extraction.
Responsible AI in Evidence Synthesis (RAISE)
- Responsible AI in Evidence Synthesis (RAISE); advice for evidence synthesists, methodologists, AI developers, and publishers to ensure ethical and transparent AI use.
- RAISE is an international initiative aimed at developing guidance for the responsible use of artificial intelligence (AI) in evidence synthesis. As AI tools become increasingly integrated into systematic reviews, scoping reviews, guideline development, and other forms of evidence synthesis, concerns have emerged regarding transparency, reproducibility, bias, accountability, and research integrity.
- The RAISE project brings together evidence synthesists, methodologists, AI developers, publishers, and other stakeholders to establish consensus-based recommendations for the ethical and transparent application of AI throughout the evidence synthesis process. The guidelines are expected to address key issues such as documenting AI use, validating AI-generated outputs, managing risks of hallucinations and bias, ensuring human oversight, and promoting reproducibility of AI-assisted workflows.
- By providing practical recommendations and reporting standards, RAISE seeks to help researchers harness the benefits of AI while maintaining the rigor and trustworthiness that underpin evidence-based decision-making. The initiative recognizes that AI has the potential to improve efficiency and reduce workload, but that its use must be carefully governed to protect the quality and credibility of synthesized evidence.
- The consensus-based RAISE guidelines are anticipated soon and are likely to become an important resource for researchers, librarians, review teams, journal editors, and publishers navigating the evolving landscape of AI-assisted evidence synthesis.
- Flemyng E, Noel-Storr A, Macura B, Gartlehner G, Thomas J, Meerpohl JJ, Jordan Z, Minx J, Eisele-Metzger A, Hamel C, Jemioło P. Position statement on artificial intelligence (AI) use in evidence synthesis across Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence 2025. Campbell Systematic Reviews. 2025 Dec;21(4):cl2-70074.
Covidence support of RAISE
- Human oversight is mandatory
- Authors retain full responsibility for the review
- AI must not compromise methodological rigor
- All AI use must be transparent and reported
References
- Allison J. RAISE the standard: A framework for transparent reporting of artificial intelligence studies in education. Journal of Educational Computing Research. 2026 Jan;64(1):3-15.
- Flemyng E, Noel-Storr A, Macura B, Gartlehner G, Thomas J, Meerpohl JJ, Jordan Z, Minx J, Eisele-Metzger A, Hamel C, Jemioło P, Porritt K, Grainger M. Position Statement on Artificial Intelligence (AI) Use in Evidence Synthesis Across Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence 2025. Campbell Syst Rev. 2025 Nov 10;21(4):e70074.
- Evidence synthesists publishing with Cochrane, Campbell Collaboration, JBI, and Collaboration for Environmental Evidence can use AI as long as they demonstrate it does not compromise methodological rigour or integrity of their synthesis;
- AI and automation in evidence synthesis should be used with human oversight;
- Any use of AI or automation that makes or suggests judgements should be fully and transparently reported;
- AI tool developers should proactively ensure their AI systems or tools adhere to the RAISE recommendations so we have clear, transparent, and publicly available information to inform decisions about whether AI should be used in evidence synthesis.
Disclaimer
- Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.
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