The PRISMA statement has been the dominant reporting standard for systematic reviews and meta-analyses since 2009, with its most recent major revision in 2020. The 2026 update, drafted through 2024 and 2025 and finalised at the end of 2025, adds machine-readability, structured handling of AI-assisted screening, and explicit support for living systematic reviews. This post walks through what changed, why it matters, and what reviewers and journals should do to update their practices.
What PRISMA 2020 left unresolved
PRISMA 2020 added much-needed clarity to several persistent ambiguities — the role of registries and protocols, transparent reporting of search strategies, structured presentation of risk-of-bias assessment — but it left several gaps that became more pressing through 2021-2024.
First, AI-assisted screening. By 2023, a substantial fraction of new systematic reviews used machine-learning tools for title-and-abstract screening (Abstrackr, Rayyan’s ML mode, Covidence’s automation, Distiller’s classification, bespoke models). PRISMA 2020 had no place to report this; reviewers either omitted it, mentioned it in passing, or invented their own reporting conventions. The result was a reproducibility gap: a reader could not tell whether a review had used AI to filter studies, what the AI’s parameters were, or how human checking was integrated.
Second, living reviews. The conventional systematic review is a snapshot: search to a date, screen, extract, synthesise, publish. A living systematic review is continuously updated as new evidence emerges. PRISMA 2020’s reporting conventions assumed a snapshot model; reviewers running living reviews had to adapt the checklist by hand.
Third, structured machine-readability. PRISMA 2020 specified what to report but not how to deposit it in structured form. The result was that systematic-review metadata lived as free text in PDFs, unreachable by tools that wanted to aggregate methodological features across reviews.
What PRISMA 2026 changes
The 2026 revision is layered: the 27-item core checklist remains, with three items extended and four new items added. The extensions are backward-compatible — a review that satisfies PRISMA 2020 also satisfies the unchanged items of PRISMA 2026 — and the new items are clearly flagged. The full statement is being published in the usual cluster of journals (BMJ, PLOS Medicine, Journal of Clinical Epidemiology, Systematic Reviews) with simultaneous open-access release.
The AI-screening item
The new item 8b requires reviewers who used AI or machine-learning tools in study identification, screening, or data extraction to report: the tool used (name and version), its training data or pre-training source, the threshold for AI-flagged inclusion versus human review, the human-checking strategy (full re-screen, sample re-screen, only AI-rejected items), and the integration into the overall workflow with quantitative reporting of agreement rates.
This is non-trivial reporting and will catch many reviews unprepared. The recommendation from the working group is that AI-screening parameters should be set in the protocol (registered on PROSPERO or an equivalent registry) before screening begins, and that the reporting follow the protocol. A review that decides post hoc to use AI screening without protocol support is on weaker ground for both methodology and reporting.
The living-review checklist
PRISMA 2026 adds a parallel reporting checklist for living systematic reviews: items covering the update frequency, the trigger for re-running the search, the handling of new evidence that changes pooled estimates, and the versioning of the published review. The checklist is meant to be applied at each update, with structured logging of what changed between versions.
For journals publishing living reviews, the implication is that they need an editorial process that supports versioned publication. The BMJ, Cochrane Library, and several others have living-review streams; many others do not, and PRISMA 2026’s existence will push more journals toward supporting the format.
The machine-readable flow diagram
The PRISMA flow diagram has been the visual centrepiece of every systematic review since 2009. PRISMA 2026 introduces a structured JSON representation alongside the visual diagram, with the diagram regeneratable from the JSON. The JSON captures: records identified per source, records duplicate-removed, records screened, records excluded with reasons categorised, reports retrieved, reports assessed for eligibility, reports excluded with reasons categorised, studies included, reports of those studies included.
The structured format means a reader (or a tool) can query the flow programmatically. The intended downstream uses include automated meta-research, evidence-synthesis platforms ingesting reviews at scale, and the construction of multi-review evidence maps from machine-readable inputs. The CASRAI reproducibility domain has begun cataloguing the JSON schema.
What journals should do
For journals publishing systematic reviews, three updates are needed. First, update the submission template to ask for PRISMA 2026 compliance (the working group has issued model wording). Second, require deposit of the machine-readable flow diagram JSON alongside the PDF; the BMJ has pioneered this and the model is straightforward. Third, accept registered living-review submissions with a path to versioned publication, even if the current editorial workflow assumes single publication.
What reviewers should do
For systematic reviewers, the practical changes are: include PRISMA 2026 compliance in your protocol and pre-register it; if you use AI screening, plan the reporting against item 8b from the outset; produce the flow-diagram JSON during the review (most modern reference-management tools will export it) rather than reconstructing it at write-up; if your review is intended to be living, declare so in the protocol with the update strategy specified.
EQUATOR Network alignment
PRISMA 2026 has been developed in close coordination with the EQUATOR Network and is the first major EQUATOR-listed reporting guideline to include both AI-assisted research conduct and machine-readable structured outputs. The expectation is that other EQUATOR guidelines (CONSORT, STROBE, ARRIVE) will follow similar patterns in their next revisions.
Areas of ongoing debate
Two questions in the PRISMA 2026 development process were not closed and deserve continued attention. First, the threshold for AI use that triggers item 8b. The current language is “any use of AI or machine learning in study identification, screening, or data extraction.” Some reviewers argued for a higher threshold — only deep-learning-based tools with non-trivial filtering thresholds — while others argued for a lower one — any automation including deduplication. The published version errs toward broader disclosure.
Second, the scope of the structured output. The flow-diagram JSON is the first machine-readable PRISMA item, but the same logic could apply to the risk-of-bias assessment, the data-extraction sheet, and the synthesis. The working group elected to start small with the flow diagram and expand in future revisions.
For the CASRAI community, the takeaway is that systematic-review reporting is moving in the direction we have argued for: structured, machine-readable, integrated with the PID infrastructure (review DOIs, protocol DOIs, dataset DOIs), and explicit about modern tooling. The remaining gaps are tractable. PRISMA 2026 is a substantial step.
