How to Build a Literature Review Matrix With AI

Write the review question before building the table. Record the dates, languages, study types, populations or contexts, outcomes, and publication types that qualify. Add an exclusion-reason field so a borderline paper does not quietly move in or out later. Keep discovery and screening decisions outside the extraction prompt until the selected set is stable.

If you are conducting a systematic review, use the protocol and reporting standard required by your field. The PRISMA 2020 checklist calls for explicit eligibility criteria, information sources, selection processes, and data-collection methods. A personal or narrative review may be less formal, but written boundaries still prevent an interesting finding from silently changing the question.

Give every included paper a short internal ID such as S01. Record its title, authors, year, journal, DOI, PMID when applicable, canonical landing page, PDF version, and access date. Resolve a DOI through its canonical doi.org address instead of treating a downloaded filename as the identity of the work. Crossref explains that a registered DOI is persistent even though the associated metadata can be updated.

For biomedical records, a PMID is also useful. The PubMed User Guide states that PMIDs do not change or get reused. Keep separate IDs for the study and the report when several papers describe the same study; otherwise a follow-up article and the primary report can be mistaken for two independent studies.

Design the columns from the review question, then pilot them on two or three different papers. Capture study and report IDs, full citation, inclusion decision, design, sample or corpus, context, method, outcome or theme, measurement and time point, reported result, limitations, funding, declared conflicts, source locator, extraction status, verifier, and date.

Use “not reported,” “not applicable,” and “unclear” as real values because a blank cell is ambiguous. The Cochrane Handbook chapter on collecting data recommends structured, piloted collection forms and enough detail to represent the source faithfully. Its clinical fields will not fit every discipline, but the underlying rule does: decide what the question needs before extraction begins.

Give the AI one selected PDF and the matrix contract. Ask it to return only the requested fields, mark absent information as “not reported,” and attach a page, section, table, figure, or supplement locator to every substantive result. Separate what the authors report from the extractor's interpretation and prohibit invented identifiers or newly calculated effects.

Save the output as a candidate row. Do not let the model compare papers yet. Paper-by-paper extraction makes source leakage easier to spot and prevents a polished cross-paper summary from hiding which PDF supplied a claim. If a table or supplement is not available to the system, mark the relevant field unclear instead of filling it from surrounding prose.

Open every locator and check the surrounding passage, table heading, footnote, and denominator. For numerical findings, verify the population, comparison, direction, unit, time point, and uncertainty measure. For qualitative findings, check that the row preserves the participant group, context, and authors' stated scope.

Mark each field verified, corrected, unclear, or excluded. High-stakes or systematic reviews may require independent duplicate extraction and a documented resolution process; Cochrane specifically recommends more than one person for critical outcome data. AI-assisted extraction does not satisfy that human independence requirement by itself.

Once rows pass verification, group them by the same outcome or theme. Add a contradiction field that classifies why findings differ: population, definition, method, measurement, time point, comparator, study design, or genuinely incompatible results. Preserve both rows and link the editorial decision that follows.

AI can propose comparison groups, but a researcher must verify them. Two papers using the same word may measure different constructs. Differently named outcomes may be comparable only after a documented coding decision. Record the reasoning instead of asking the model to choose a winner or average incompatible results into a tidy conclusion.

Build the review around questions or themes rather than paper order. Each synthesis paragraph should point back to the study IDs that support it, distinguish reported results from your interpretation, and carry unresolved disagreement forward. Reopen the cited rows during the final fact check and follow every locator to the PDF.

Use subject databases to discover the scholarly corpus and a reference manager to store citations and format the bibliography. Use the matrix to preserve eligibility, methods, findings, limitations, locators, and comparison decisions. These tools solve different parts of the job; the matrix stays useful because it makes the path from synthesis back to the selected paper explicit.