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~15 min with AI, ~2–3 hours without Standard review of search strategy and selected sources.Research question → Search strategy → Database search → Screen results → Evidence set

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Best for

  • Starting a new publication or medical affairs project that needs a defined evidence base
  • Background research for advisory board materials, slide decks, or training content
  • Identifying key papers for a literature review or competitive landscape analysis
  • Building an evidence set to support key message development or publication planning
  • Screening a large set of abstracts to find the most relevant sources quickly

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Inputs

  • A clearly defined research question, indication, compound, or topic area
  • Any known key references or authors to use as starting points
  • Inclusion/exclusion criteria for the evidence you need (publication date, study type, population)
  • The intended use of the evidence (informs how broad or narrow the search should be)

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Steps

1

Define the research question

Be specific. “What is the efficacy and safety of Drug X in moderate-to-severe plaque psoriasis?” will produce better results than “Drug X psoriasis.” Specify the population, intervention, comparator, and outcomes you need (PICO framework).
2

Build the search strategy

Use AI to generate candidate search terms, MeSH headings, and Boolean combinations. Review and refine these manually. A poorly constructed search returns noise; a well-constructed one saves hours of screening.
3

Run the search

Execute the search across relevant databases (PubMed, Embase, trial registries, prescribing information). Use PubCrawl for structured biomedical searches or Perplexity for quick exploratory queries with cited sources.
4

Screen results

Review titles and abstracts against your inclusion criteria. AI can help flag likely relevant results, but the decision to include or exclude a paper is yours. Pay attention to study design, population, and recency.
5

Evaluate and select

Read the full text of shortlisted papers. Assess relevance, quality, and how each paper fits the project’s evidence needs. This is where editorial and scientific judgement matters most.
6

Organise the evidence set

Structure your selected sources for downstream use. Note each paper’s key contribution to the project (primary efficacy data, safety profile, comparator data, real-world evidence). Store and cite references using a reference manager.

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Output

A curated set of 5–30 source documents (depending on project scope) organised by relevance, with a brief note on each paper’s contribution to the project. The evidence set should be traceable back to the search strategy and inclusion criteria, and sufficient to support the downstream writing workflows.

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Prompt pattern

Customisation: For systematic-style searches, add a PRISMA-aligned instruction. For competitive landscape work, add comparator compounds and ask for head-to-head trial search terms.

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Why this works

AI generates comprehensive search strategies in minutes, surfacing MeSH terms, author names, and Boolean combinations that a manual approach might miss. The human writer retains the decisions that determine evidence quality: defining the research question, setting inclusion criteria, evaluating study relevance, and judging whether the evidence set is sufficient for the project.

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Common mistakes

“What is known about Drug X?” returns hundreds of results across indications, populations, and study types. Narrow the question before searching. A 30-second refinement saves an hour of screening.
AI can rank and flag abstracts, but it cannot judge whether a particular study design is appropriate for your project, whether the population matches your target, or whether the journal is credible. Source selection is a human decision.
PubMed does not index everything. For certain therapeutic areas, Embase, Cochrane, or trial registries (ClinicalTrials.gov, EU CTR) may contain essential evidence that PubMed misses. Match your database selection to the project requirements.
An abstract may suggest a paper is relevant, but the full text may reveal a different population, a post-hoc analysis, or an endpoint that does not match your needs. Always read the full text of shortlisted papers before including them in your evidence set.
If someone asks how you found your evidence, you should be able to show the search terms, databases, date range, and inclusion criteria. Undocumented evidence gathering cannot be audited or reproduced.

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Tool stack

Alternatives: Claude Cowork for analysing multiple papers together once your evidence set is assembled. NotebookLM for exploring and questioning uploaded papers. Elicit for structured paper extraction and synthesis. Consensus for fast research question exploration. Perplexity for quick fact-checking with cited sources. Scite.ai for citation context across the literature. Zotero or EndNote for storing and organising the evidence you find.

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Frequently asked questions

Yes — AI is well-suited to generating candidate MeSH headings, Boolean combinations, and related search terms. Treat the output as a first draft. Review every term, confirm the logic maps to your research question, and run the final search in PubMed or Embase directly rather than relying on AI to execute it.
It depends on the tool. Many chat interfaces only see the abstract unless you upload the full PDF. For medical writing work, always provide the full text. An abstract summary is not a substitute for the Methods, Results, and Limitations sections that actually determine whether the paper is suitable.
AI can flag likely relevant abstracts faster than a manual pass, but the decision to include or exclude a paper is yours. Use it as a ranking aid, not a filter. Always verify that flagged papers match your inclusion criteria by reading the abstract yourself, and spot-check the rejected pile.
Completeness depends on project scope. For a review or publication, the set should cover the primary endpoints, key comparators, safety profile, and relevant real-world or landscape context. If a senior reviewer could name an obvious paper you have missed, the set is not complete.
Embase, Cochrane, and trial registries (ClinicalTrials.gov, EU CTR) for most therapeutic areas. For health economics or real-world evidence, add HEOR-specific sources. Congress abstracts may only appear on society websites. Match database selection to the deliverable, not to convenience.

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Review checklist

  • The research question is clearly defined and specific enough for the project
  • Search terms and Boolean strategy are appropriate and comprehensive
  • Relevant databases have been searched (not just PubMed)
  • Inclusion and exclusion criteria are documented
  • Abstracts have been screened against the criteria
  • Full texts of shortlisted papers have been reviewed
  • The evidence set is sufficient for the project’s scope and objectives
  • Key papers in the therapeutic area have not been missed
  • The search strategy and results are documented for audit

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Further reading

For a conceptual example of how multiple AI technologies — RAG, generative AI, multimodal systems, and AI agents — can support pharmaceutical workflows, see A Day in the Life of an MSL Powered by AI: Combining AI Technologies to Transform Training — published in Journal of Next-Generation Research 5.0.
Next steps: Use your evidence set to Summarise a Source Paper, Prepare a Congress Summary, or Extract Study Data, then Extract Key Messages for content development.
Last reviewed: 15 April 2026