Should a research team use AI to help screen literature?
The team wants to reduce screening effort while keeping every included claim traceable to its source. The initial scope uses public papers and excludes unpublished research or patient data.
- Options to compare
- The existing reference-manager and manual-review workflow; an AI-assisted workflow with human review. Specific tools would be selected against the team's requirements.
- Decision criteria
- Relevant-paper coverage, traceable citations, correction effort, export options, data-handling terms and total cost for the intended users.
- Evidence still needed
- Dated official documentation, current pricing and terms, plus results from testing representative public papers. Vendor claims and observed results would be identified separately.
- Questions before a pilot
- Who checks the outputs? Can source passages be inspected? What can be exported? What happens to uploaded text? Which features require a paid plan?
Proposed next step for this scenario
Run a limited comparison using an agreed set of public papers and reference answers. Record errors, omissions, correction effort and total review time for each workflow.
Agree quality, effort and cost thresholds before the trial. Use the results to decide whether to proceed, revise the workflow or keep the existing approach.
A commissioned brief would contain the findings and sources appropriate to its agreed scope. This example does not establish that an AI tool saves time or is suitable for your work.