Developing a fully automated evidence synthesis tool for identifying, assessing and collating the evidence

Evidence synthesis is a key element of evidence-based medicine. However, it is currently hampered by being labour intensive meaning that many trials are not incorporated into robust evidence syntheses and that many are out of date. To overcome this, a variety of techniques are being explored, including using automation technology. Here, we describe a fully automated evidence synthesis system for intervention studies, one that identifies all the relevant evidence, assesses the evidence for reliability and collates it to estimate the relative effectiveness of an intervention. Techniques used include machine learning, natural language processing and rule-based systems. Results are visualised using modern visualisation techniques. We believe this to be the first, publicly available, automated evidence synthesis system: an evidence mapping tool that synthesises evidence on the fly.

Automated summary

Evidence synthesis is essential for evidence-based medicine, but it currently faces challenges such as labor intensiveness and outdated information. To address this, techniques like automation technology, machine learning and natural language processing are being explored to develop a fully automated evidence synthesis system for intervention studies. This system aims to identify, assess and collate relevant evidence to estimate the effectiveness of interventions, providing a real-time evidence mapping tool.