Systematic reviews are the gold standard for evidence synthesis in clinical research: they apply a pre-registered protocol to comprehensively retrieve, appraise, and synthesise evidence on a clinical question. Their scientific rigour is also their practical limitation — a comprehensive systematic review typically takes twelve to eighteen months to complete, by which time the evidence base may have shifted and the clinical decision it was designed to inform may already have been made. Knowledge graph-assisted systematic reviews maintain the methodological standards while compressing the timeline.
The Five Most Time-Consuming Steps
The five most time-consuming steps in a systematic review are: (1) designing and executing the literature search; (2) screening titles and abstracts for inclusion eligibility; (3) full-text review of eligible studies; (4) data extraction from included studies; and (5) risk of bias assessment. Knowledge graph assistance most directly addresses steps one and four. The literature search, as described in other articles in this series, benefits immediately from ontology-driven concept expansion. Data extraction — the mechanical recording of study design characteristics, population parameters, outcomes, and effect sizes — can be automated using NLP pipelines trained on systematic review data extraction tasks, with human expert review focused on ambiguous or complex cases.
Living Systematic Reviews
One of the most significant opportunities opened by knowledge graph-assisted systematic reviews is the living systematic review model: a review that is continuously updated as new evidence is published, rather than performed once and published as a static document. When the literature search, screening, and data extraction steps are automated, the barrier to adding new evidence to an existing review drops from months to days. The knowledge graph serves as the persistent evidence store, with each new study's data extraction appended to the existing record and the synthesis updated programmatically.
Regulatory Applications
Regulatory submissions increasingly reference systematic reviews as part of the clinical evidence package. Submissions that include a living systematic review — one that is demonstrably current as of the submission date, with a documented automated search and update process — provide regulators with a more reliable evidence picture than a static review conducted twelve months before submission. The infrastructure investment in knowledge graph-assisted systematic review methodology pays dividends across every indication development programme that can build on the same underlying evidence retrieval and extraction systems.
