Starting AI‑assisted systematic reviews

Why AI for evidence synthesis?

Systematic reviews are full of repetitive, structured work: formulating searches, screening records, extracting results, checking quality, and assembling a manuscript. AI can take on the heavy lifting. The review still has to stand up to a colleague, a journal, or a methods audit.

That only works if every important agent decision stays inspectable, challengeable, and tied to a human checkpoint.

What inspectable means here

• Show the source quote, matched criterion, confidence, agent, and timestamp for a decision.
• Make it obvious when a human has approved, overridden, or sent work back.
• Keep artefacts versioned so a later reader can see what changed and why.

If a screening include, extracted number, or manuscript claim cannot be traced, it does not belong in a defensible review.

Human checkpoints stay in the loop

AI can propose a search block, a screening decision, or a results paragraph. A person still has to accept the protocol, confirm controlled vocabulary, resolve uncertain full texts, and sign off before work moves downstream.

The useful split is mechanical drafting versus scientific judgement. Models are strong at the first. They are not a substitute for the second.

What to expect in this blog

Short notes on search construction, screening ergonomics, extraction patterns, and manuscript assembly — with a bias toward workflows that stay transparent end to end.