Evidence synthesis has substantially improved effectiveness in fields like medicine through systematic review processes. AI has the potential to speed up searching, filtering, and identifying issues within papers. However, concerns arise that AI may produce fake papers, threatening the integrity of evidence synthesis. A need for ongoing, updated evidence databases is proposed to maintain responsiveness in research. Systematic reviews, although rigorous, are resource-intensive but remain the gold standard in gathering high-quality evidence while mitigating bias.
The process of systematically combining findings from multiple studies into comprehensive reviews helps researchers and policymakers to draw insights from the global literature.
AI promises to speed up parts of the process, including searching and filtering, and could help researchers to detect problematic papers.
We propose a network of continually updated evidence databases, hosted by diverse institutions as 'living' collections, to enhance the reliability and responsiveness of evidence syntheses.
The gold standard for evidence synthesis is the systematic review, which aims to include as much relevant high-quality evidence as possible while reducing bias.
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