Pull-quote: “A review that is six months out of date is not a review. It is a historical artifact.”

Why this matters

The fundamental flaw of the traditional systematic review is that it is a snapshot. A team works on it for six months, freezes the literature search at a date, and publishes a result that becomes outdated the moment the next paper appears. In rapidly evolving fields — oncology, infectious disease, AI/ML methodology, certain rare-disease indications — that lag is unacceptable.

The fix is a living systematic review — a review that is continuously refreshed as new evidence appears.

What “living” actually requires

Living reviews are not just “running the search again every quarter.” They require:

1. Protocol stability — the inclusion / exclusion criteria do not change between updates
2. Federated search at scheduled cadence across the full database set
3. Delta detection — what’s new since the last update
4. Consistent screening — the same multi-agent consensus applied to new papers
5. Risk-of-bias and GRADE re-assessment — if a new high-quality study changes the certainty of evidence, that needs to surface
6. Versioned reporting — each refresh produces a versioned report with a clear changelog
7. Subscriber notification — stakeholders are alerted when something material changes

This is not a research methodology improvement. It is an engineering problem: how to do high-rigor evidence synthesis on a recurring schedule, with reproducibility and auditability preserved.

Architecture

EvidAI’s living review architecture:

Protocol (versioned) ──► Federated search (11 databases, scheduled)
                                          │
                                          ▼
                              Delta detection
                                          │
                          New papers since last refresh
                                          │
                                          ▼
                       Multi-agent consensus screening
                                          │
                          Included papers (new)
                                          │
                                          ▼
                  Risk-of-bias (RoB 2 / ROBINS-I / NOS)
                                          │
                                          ▼
                  GRADE re-assessment per outcome
                                          │
                                          ▼
                  Living report (versioned, with changelog)
                                          │
                                          ▼
                  Subscriber notifications

What changes for the team

The team’s role shifts from “run a six-month review every two years” to “monitor a continuously updated review and adjudicate the small fraction of decisions the AI escalated.” That is a fundamentally different work pattern, and it scales.

Closing

A review that is six months out of date is not a review. Living reviews are an engineering solution to a research methodology problem — and they are now operationally feasible.

The post Living Systematic Reviews: Evidence That Stays Current appeared first on Zorost Intelligence | AI, Cloud & Data Experts.

Pull-quote: “If four independent reasoners agree, the inclusion decision is high-confidence. If they disagree, the question goes to a human. That’s the design contract.”

Why this matters

Systematic literature reviews underpin regulatory submissions, clinical practice guidelines, and HTA decisions. Doing them well is expensive and slow — typically 4–6 months and a six-figure investment for a single review. Doing them badly is dangerous.

The first wave of LLM-assisted screening was a single model judging each title/abstract against the inclusion criteria. It was faster than manual review. It was no more accurate. In some cases, it was less accurate, because a single model has systematic biases that a human reviewer doesn’t share.

What multi-agent consensus does

EvidAI runs every screening decision through four independent LLMs, each with a structured prompt that includes the protocol’s inclusion and exclusion criteria, a brief excerpt from the abstract, and a request for explicit reasoning.

The four models vote. Three patterns emerge:

(Frequencies are typical for a well-designed protocol; they vary with topic.)

Why the design works

The key insight is that independent errors are uncorrelated. Different LLMs have different systematic biases — different training data, different RLHF preferences, different prompt sensitivities. When four independent reasoners agree, the marginal probability of error drops sharply. When they disagree, the model designers’ expected behavior is reproducing the disagreement that human reviewers would have had — which is exactly what should be escalated.

Single-model screening hides disagreement. Multi-agent consensus surfaces it.

Auditability

Every screening decision is stored as a row with: paper ID, protocol version, model identifiers, raw model outputs, parsed decisions, the reason for inclusion/exclusion in each model’s words, the consensus result, and (if applicable) the human reviewer’s adjudication. The complete chain is replayable by an auditor and reproducible by a successor team.

This is the difference between an AI tool that speeds up the SLR process and one that preserves the audit standard it requires.

Closing

The multi-agent consensus pattern is the right answer for any high-stakes screening problem where accountability and auditability matter. EvidAI applies it to systematic reviews. The same pattern transfers cleanly to compliance screening, regulatory document review, due diligence, and grant assessment.

The post Multi-Agent Consensus for Systematic Literature Review appeared first on Zorost Intelligence | AI, Cloud & Data Experts.