Evidence synthesis — the systematic aggregation of clinical evidence from multiple studies to support a regulatory submission, a clinical guideline, or a therapeutic decision — is one of the most time-consuming expert tasks in pharmaceutical development. A clinical overview for a marketing authorisation application may require the synthesis of evidence from hundreds of published studies, dozens of clinical study reports, and years of post-marketing surveillance data. The scientific judgement at the core of this task requires human expertise; the retrieval and structuring of the underlying evidence does not.
The RAG Architecture for Evidence Synthesis
A retrieval-augmented generation architecture for evidence synthesis typically combines three components. A knowledge graph stores curated assertions about clinical evidence: study designs, patient populations, endpoints, results, and their supporting citations. A retrieval system queries the knowledge graph in response to synthesis questions, returning the most relevant evidence records with their provenance. A language model synthesises the retrieved evidence into structured prose, following a template appropriate to the document type being produced.
Where Automation Adds Value
The retrieval and initial structuring phases of evidence synthesis are well-suited to automation. Given a question such as "summarise the safety profile of compound X in patients with renal impairment", the system can retrieve all relevant adverse event records, clinical study results, and label information from the knowledge graph, extract the relevant evidence attributes, and generate a first-pass structured summary that captures the major findings and their sources. Human reviewers then focus on critical evaluation of the evidence — assessing study quality, identifying contradictions, and making interpretive judgements — rather than on the mechanical task of assembling the evidence inventory.
Auditability Requirements
For regulatory applications, the audit trail of an AI-assisted evidence synthesis must demonstrate that no relevant evidence was excluded and that included evidence was accurately represented. This requires that every claim in the synthesised output be traceable to a specific source record in the knowledge graph, and that the query logic used to retrieve evidence be fully documented. Systems that provide this level of traceability are building the infrastructure for a future where AI-assisted regulatory submissions are not merely accepted but expected.
