The Tool Desk
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The safest workflow separates exploratory discovery from systematic evidence synthesis. Record the tool and version, prompts, dates, sources, filters, and corrections; protect confidential material; and follow the disclosure rules of the journal, institution, funder, or review method that governs your work.
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What an AI research tool actually does
AI research tool is an umbrella term, not a quality label. Products may perform semantic discovery, conventional bibliographic search, study screening, data extraction, evidence synthesis, or writing assistance. A system that is useful for finding related terminology has not automatically been shown to identify eligible trials, extract outcomes accurately, or judge certainty of evidence.
| Research stage | Where AI can help | What still requires checking |
|---|---|---|
| Semantic discovery | Suggest synonyms, adjacent concepts, influential authors, and candidate papers. | Whether the papers are real, relevant, available in the required date range, and representative of the field. |
| Bibliographic searching | Translate a question into keywords, subject terms, and alternative query formulations. | Database coverage, indexing differences, Boolean logic, filters, duplicates, and missed studies. |
| Screening | Prioritize titles and abstracts or apply a preliminary label for human review. | Eligibility decisions, edge cases, disagreements, and false exclusions. |
| Extraction and synthesis | Locate passages, place reported values into a draft table, or compare study characteristics. | Numbers, denominators, units, context, risk of bias, heterogeneity, and the difference between association and causation. |
| Writing support | Outline notes, clarify prose, or identify unsupported sentences in a draft. | Every factual statement, quotation, citation, interpretation, and disclosure. |
Suitability is therefore task-specific. Evidence that a tool performs acceptably on one collection of studies or one language does not validate it for another discipline, population, outcome, or workflow.
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Step 1: Define the research question before opening an AI tool
A vague prompt produces a broad, hard-to-audit list. Write a short protocol before searching. Specify the population or subject, phenomenon or intervention, comparator when relevant, outcomes, date range, and discipline or setting. State whether the goal is exploratory discovery or a systematic evidence synthesis; those are different jobs with different requirements.
Turn a topic into an answerable brief
For an intervention question, describe who receives it, what it is compared with, and which outcomes and follow-up matter. For an observational question, define the exposure, population, outcome, and study design of interest. For a methods or theory question, state the concepts, field, and kinds of sources you will include. Add language, geography, publication-type, and access constraints only when they are genuine requirements.
Write inclusion and exclusion rules in plain language. For example: include peer-reviewed studies of adults with condition X, published from 2018 through 2025, that measure outcome Y against usual care; exclude editorials, animal studies, and papers without a usable comparator. A rule written before seeing results reduces the temptation to change eligibility because a paper is interesting.
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Decide how much completeness you need
An exploratory scan can use AI to map terminology and identify starting points. It is not a claim that all relevant evidence was found. A systematic review or other formal synthesis needs a reproducible search strategy, documented databases and dates, predefined eligibility criteria, duplicate handling, and a defensible process for screening and appraisal. Do not present an informal AI-assisted search as exhaustive.
Step 2: Use AI to generate leads and organize them
Give the tool your research brief and ask for candidates, not conclusions. Request synonyms, spelling variants, controlled-vocabulary suggestions, related mechanisms, landmark terminology, and possible database query blocks. Ask it to separate exact matches from adjacent concepts and to explain why each suggestion might be relevant.
A discovery prompt that preserves boundaries
Research question: [state the population, intervention or exposure, comparator, outcome, date range, and field]. Goal: exploratory discovery, not a systematic review. Suggest search terms in separate groups for population, intervention or exposure, comparator, and outcome. Identify ambiguous terms and likely synonyms. For each candidate paper you mention, provide the exact title and a reason to verify it; do not invent citations.Even with that instruction, treat every title, author, journal, year, DOI, and summary as an unverified lead. Save the original output with the date and tool version. If the tool offers links, open them rather than assuming a clickable result is authentic.
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Build a reproducible search record
- Record the tool name, model or feature, version if shown, account or workspace settings that affect retrieval, and the date and time.
- Save the exact prompts and the returned suggestions, including rejected terms that explain how the vocabulary evolved.
- List each database, index, repository, or publisher search used, with its query, filters, language limits, and date range.
- Export results in a stable format, assign an identifier, and record deduplication decisions.
- Mark whether a record came from an AI suggestion, a database query, citation chaining, or a human expert.
This record lets another researcher distinguish what the tool proposed from what the scholarly search actually retrieved.
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Step 3: Verify every reference in an authoritative scholarly record
Never cite a generated bibliography without checking the original record. Open the publisher page, a trusted disciplinary index, or an institutional repository copy. Confirm the bibliographic fields and then check the cited passage itself.
- Locate the record. Search the exact title in a recognized scholarly database or the publisher or repository site. If no authoritative record exists, treat the reference as unverified and do not cite it.
- Match the identity. Confirm the full title, all relevant authors, journal or conference, publication year, volume and issue when applicable, pages or article number, and DOI or other persistent identifier.
- Check the version. Distinguish a preprint, protocol, correction, retraction, conference abstract, accepted manuscript, and final article. Use the version appropriate to your claim and disclose it when material.
- Read the cited location. Follow the page, section, table, figure, or paragraph that supposedly supports the statement. A fluent summary can combine findings from different papers or reverse the direction of an effect.
- Record the result. Mark the citation as verified, partially supports the claim, does not support it, or could not be checked. Preserve the reason for any exclusion or correction.
The US Centers for Disease Control and Prevention warns that citation inaccuracies can be considered research misconduct. That warning applies even when an error came from an apparently helpful automated answer. Correct metadata is necessary but not sufficient: the cited study must actually support the wording you publish.
Step 4: Read the primary study for claims that matter
Use the abstract for triage, then inspect the full paper whenever a claim affects a conclusion, recommendation, safety statement, or quantitative result. Separate what the authors report from what an AI system infers across several papers.
Minimum reading checklist
- Design and methods: identify the design, recruitment, intervention or exposure definition, comparator, duration, measurements, and prespecified outcomes.
- Population and sample: check eligibility, sample size, attrition, demographic or geographic limits, and whether the participants match your question.
- Results: verify effect estimates, uncertainty intervals, denominators, missing data handling, and whether a result was primary, secondary, or exploratory.
- Interpretation: distinguish association from causal evidence and statistical significance from practical importance.
- Limitations: note confounding, selection, measurement error, imprecision, conflicts, funding, and authors’ stated limits on generalization.
- Corrections and retractions: check the current record before relying on a result that has been widely circulated.
When an AI summary says that a treatment works, ask whether the underlying study was randomized, whether the comparison was appropriate, and whether the outcome was measured as claimed. When it says studies agree, inspect study populations, definitions, and follow-up rather than accepting a cross-paper average without methodological justification.
Step 5: Treat screening and evidence synthesis as higher-risk uses
Screening, extraction, risk-of-bias appraisal, certainty assessment, and synthesis can change which studies enter a result and how strong the final conclusion appears. Validate an AI system for the exact task before relying on it. Ask what studies and languages were evaluated, how errors were measured, how false inclusions and exclusions were handled, and whether a reviewer can inspect the evidence behind every suggestion.
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A 2025 joint position statement from Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence says that evidence synthesis teams may use AI and automation only when they can demonstrate that methodological rigor and integrity will not be compromised. The statement expects human oversight; it is guidance for formal evidence synthesis, not a universal rule for every casual literature search.
Cochrane’s tool-selection guidance recommends proceeding with mitigations for large-language-model or generative-AI tools in evidence synthesis. Look for independent validation, known red flags, understood error patterns, and limitations that your team can manage. A vendor’s claim that a tool is accurate is not independent validation.
Use a two-pass review for automated screening
- Run the tool only on a defined, exported set of records and preserve its labels and confidence information.
- Have a human reviewer inspect all records the tool marks as eligible, uncertain, or low confidence.
- Sample records marked ineligible and review them for false exclusions, especially unusual terminology, non-English titles, and older studies.
- Compare the tool’s decisions with an independently screened subset and calculate or report the error measures appropriate to your protocol.
- Resolve disagreements under the prespecified rule and keep the adjudication trail.
Do not let an unvalidated model silently remove studies or write a pooled conclusion. If the tool cannot expose the source text, decision rationale, and correction path, it is a poor fit for a consequential synthesis.
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A person must own the research judgment. Disclosure is especially important when AI makes or suggests eligibility decisions, risk-of-bias ratings, data extraction, synthesis, certainty assessments, or claims about the overall strength of evidence. Record the tool and version, purpose, stages affected, validation performed, verification steps, and known limitations.
Requirements differ by journal, review organization, institution, funder, and jurisdiction. Check the target publication’s current instructions before submission. The American Psychological Association’s guidance, updated in August 2025, specifically recommends disclosure when AI generates a resource list for a literature review and advises preserving the output. JAMA Network guidance also addresses reporting AI use in research and scholarly publication. Follow the stricter applicable rule when requirements conflict, and retain the original outputs so an editor or collaborator can audit what happened.
What a transparent methods note should answer
- Which tool, model, or feature was used, and which version or access date applies?
- Was it used for vocabulary generation, searching, screening, extraction, analysis, translation, or prose editing?
- What data and prompts were supplied, and were any confidential or identifiable materials excluded?
- How was performance checked for this task, and who reviewed and corrected the output?
- Which decisions remained entirely human, and where are the audit records stored?
Do not claim that AI was merely used for grammar if it proposed studies, selected evidence, or altered an interpretation. The disclosure should describe the real effect on the work.
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Step 7: Protect confidential data and preserve provenance
Do not paste unpublished manuscripts, identifiable participant information, restricted datasets, peer-review materials, or confidential correspondence into a tool unless its terms and your institution, ethics approval, contracts, law, and privacy policy permit it. Check retention, training use, administrator access, deletion, geographic processing, and licensing terms. A tool that is convenient for public abstracts may be unsuitable for a dataset covered by a data-use agreement.
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How to compare tool options without chasing a universal winner
The available evidence does not establish a single best product across disciplines or stages. Compare candidates against the job you need done.
| Criterion | Questions to ask | Warning sign |
|---|---|---|
| Task fit | Was the system designed and evaluated for discovery, searching, screening, extraction, synthesis, or writing? | One broad accuracy claim is used to cover unrelated tasks. |
| Source coverage | Which databases, publishers, repositories, languages, and publication types are indexed? How current are they? | Coverage is undisclosed or limited to a narrow, opaque corpus. |
| Validation | Are there independent evaluations on comparable studies and realistic workloads? Are false positives and false negatives reported? | Only demonstrations, testimonials, or vendor-selected examples are provided. |
| Traceability | Can you open the underlying paper and the exact passage, table, or record used? | Citations are missing, broken, or impossible to inspect. |
| Human control | Can reviewers override a label, annotate a reason, export decisions, and audit changes? | Automated decisions are hidden or difficult to reverse. |
| Bias and error | What languages, disciplines, study designs, and terminology cause omissions or distortions? | There is no error analysis or uncertainty information. |
| Reproducibility | Can you save prompts, versions, search settings, outputs, and timestamps? | Results change without a record of the inputs or system version. |
| Privacy and licensing | What happens to submitted text, and may you legally store or redistribute the output? | Retention, training, or rights terms are unclear. |
| Total effort | How much review and correction time remains after the apparent time saving? | A fast first pass creates a larger verification burden than manual work. |
A systematic review of generative AI in evidence synthesis searched five databases on 17 January 2025 and assessed accuracy, error rates, time, and risk of bias. That approach is a useful model: compare measured outcomes for your task rather than treating marketing language as a ranking. It does not provide a universal accuracy percentage or a permanent best-in-class list.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common failure modes and recovery steps
The paper or DOI does not exist
Search the exact title, author combination, and identifier in an authoritative index. Check spelling and whether the tool merged two records. If no reliable record can be found, delete the citation and revisit the original prompt; never repair a fabricated reference by guessing a DOI.
The citation exists but does not support the sentence
Read the cited passage and narrow the sentence to what the study actually measured. If the claim requires a different population, outcome, or causal interpretation, find a source that addresses that requirement or remove the claim.
The tool misses relevant terminology or whole groups of studies
Compare its suggestions with controlled vocabularies, expert input, backward and forward citation searching, and more than one database. Test the strategy on known seminal or sentinel papers. Add synonyms and rerun the documented search rather than assuming semantic similarity provides complete coverage.
The summary collapses disagreement into consensus
Create a study-by-study table of design, population, outcome definition, estimate, uncertainty, and limitations. Explain why results differ. Do not use an AI-generated overall statement until the underlying studies have been inspected.
Screening automation removes an eligible study
Audit false exclusions with a random sample and a targeted sample of unusual titles. Increase human review, revise the eligibility instructions, and rerun the affected records. Report the change and its effect on the included set.
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Stop further uploads, follow your institution’s incident and data-protection procedure, document what was sent and when, and ask the service provider about deletion under the applicable terms. Do not assume that deleting a chat removes retained copies or backups.
Three practical workflows
Exploratory topic mapping
- Write a one-paragraph question and list the concepts that must be present.
- Ask AI for synonyms, neighboring concepts, and candidate papers, clearly labeling the task as exploratory.
- Verify a small set of high-value papers in authoritative records and use their terminology to search databases directly.
- Expand through references and citations, recording which path found each item.
- Stop calling the result a literature review unless you have used a method appropriate to that claim.
Formal review or evidence synthesis
- Register or otherwise freeze the question, eligibility criteria, outcomes, and analysis plan required by your method.
- Run documented database searches and export the complete result set.
- Use AI only where its task-specific validation, human controls, and privacy terms are acceptable.
- Keep human review for eligibility, disagreements, extraction checks, risk-of-bias judgments, and synthesis decisions.
- Verify every included study and extracted value against the primary record.
- Report AI involvement, validation, corrections, limitations, and preserved outputs according to the governing guidance.
Updating an existing review
- Start from the prior review’s search date, included studies, exclusions, and terminology.
- Use AI to identify changed terminology, new indexing language, and possible new papers, not to assume that an old search remains complete.
- Check retractions, corrections, newer follow-up reports, and duplicate publications.
- Document exactly which date range and databases were added and how new evidence changed the conclusion.
An audit-trail template you can reuse
| Field | Example content to preserve |
|---|---|
| Question and scope | Population, intervention or exposure, comparator, outcomes, discipline, date and language limits, exploratory or systematic purpose. |
| Tool record | Provider, model or feature, version, workspace, access date, and relevant settings. |
| Inputs | Exact prompts, uploaded public documents, database queries, filters, and retrieval dates. |
| Outputs | Raw suggestions, labels, summaries, citations, confidence information, and exported files. |
| Verification | Authoritative record, identifier, passage checked, support status, correction, and reviewer. |
| Decisions | Eligibility, extraction, appraisal, synthesis, and adjudication with reasons. |
| Privacy and rights | Data classification, permission basis, retention setting, deletion action, and licensing limits. |
| Disclosure | Where AI use is described in the protocol, methods, acknowledgements, or submission forms. |
What current guidance does—and does not—establish
Authoritative guidance now supports cautious, supervised use rather than unrestricted automation. The 2025 multi-organization position statement requires evidence synthesists to demonstrate that AI will not compromise methodological rigor or integrity. Cochrane’s accompanying advice is to proceed with mitigations and examine validation and red flags. APA guidance updated in August 2025 calls for disclosure in relevant literature-review uses, including AI-generated resource lists. CDC guidance highlights the misconduct risk of citation inaccuracies, and JAMA Network provides reporting guidance for AI in research and scholarly publication.
These sources do not establish one accuracy rate for all tools, disciplines, languages, or versions. Tool behavior, index coverage, privacy terms, and journal policies change. Recheck the applicable instructions and the current tool documentation when you begin a project or submit a manuscript.
Final decision checklist
- Is the question specific enough to define what counts as relevant?
- Have you separated exploratory discovery from a claim of systematic coverage?
- Does the selected tool have evidence for this exact task and population of studies?
- Can a reviewer inspect the underlying scholarly record and correct every output?
- Have you checked each citation, passage, number, version, and interpretation?
- Are human reviewers accountable for screening, appraisal, extraction, and synthesis decisions?
- Are confidential and identifiable materials protected under applicable rules?
- Can another researcher reconstruct the prompts, searches, versions, dates, and corrections?
- Have you disclosed consequential AI use under the target journal or review guidance?
When those answers are yes, AI can reduce the mechanical burden of discovery and organization without transferring scholarly judgment to an opaque system. When they are no, use the tool only for low-stakes brainstorming or do the task manually until the missing controls are in place.
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