AI for literature review and evidence synthesis

A literature review lives or dies on its sources. An AI tool that summarizes well but cites loosely is worse than no tool at all, because it hides the gaps. The question is not whether AI can read fast; it is whether every claim it produces traces back to a source you can verify.

Where AI literature review usually breaks

General assistants are good at fluent summaries and bad at provenance. They blur which finding came from which paper, paraphrase past what a study actually claimed, and occasionally cite work that does not exist. For a quick orientation that is tolerable. For evidence synthesis, where the citation is the deliverable, it is not.

What grounded retrieval changes

medground treats the citation as a first-class requirement, not an afterthought. Answers are built from a finite, inspectable corpus, every claim carries a paper ID, and a deterministic check confirms each citation exists before the answer is returned. You end up with a synthesis where each line is attributable.

• Retrieve from a corpus you control, not from model memory.
• Read each claim with its source attached, so you can spot-check fast.
• See where studies disagree instead of having conflict smoothed into a false consensus.

A grounded review workflow

The loop is simple and repeatable:

• Frame the question and retrieve the relevant evidence.
• Draft a synthesis as discrete, cited claims.
• Run the grounding check and repair anything uncited.
• Follow citations back to the papers to confirm the reading.

Who it is for

Researchers scoping a field, teams preparing structured or systematic reviews, and clinical groups assembling evidence for a discussion all need the same thing: speed without losing the audit trail. Grounded retrieval gives you both.

Frequently asked questions