RT Journal Article SR Electronic T1 Developing a fully automated evidence synthesis tool for identifying, assessing and collating the evidence JF BMJ Evidence-Based Medicine JO BMJ EBM FD BMJ Publishing Group Ltd SP 24 OP 27 DO 10.1136/bmjebm-2018-111126 VO 26 IS 1 A1 Jon Brassey A1 Christopher Price A1 Jonny Edwards A1 Markus Zlabinger A1 Alexandros Bampoulidis A1 Allan Hanbury YR 2021 UL http://ebm.bmj.com/content/26/1/24.abstract AB 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being labour intensive meaning that many trials are not incorporated into robust evidence syntheses and that many are out of date.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.