Improving Evidence Synthesis with Artificial Intelligence

Abstract

Scientific knowledge is represented by approximately 3.3 million new journal articles each year and is expanding at an unprecedented pace, increasing in total size by 59% between 2012 and 2022 [1]. Systematic reviews and meta-analyses provide a structured means of evidence synthesis, but they are slow and labor-intensive, often requiring more than a year to complete. This bottleneck constrains scientific progress and is especially consequential in contexts such as public health crises (e.g., the COVID-19 pandemic), where timely evidence is essential for guiding policy and practice [2, 3]. Here we show that artificial intelligence methods can substantially improve both the efficiency and accuracy of systematic reviews. Using diverse datasets and examining over 30,000 data points, our AI-assisted approach matched or exceeded human performance while greatly reducing the risk of overlooking relevant evidence. In multiple tests of screening performance, the AI achieved 97.2% sensitivity and 96.84% specificity. With respect to extraction, the AI obtained 96.96% extraction accuracy, outperforming human efforts, and completed tasks up to 99% faster. These results demonstrate that AI augmentation can enable more timely and comprehensive evidence synthesis, facilitate living systematic reviews, and better support researchers, policymakers, and practitioners in responding to fast-moving scientific developments. Integrating AI into evidence synthesis represents a decisive advance in the accumulation of scientific knowledge.

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