Meghan Oates-Zalesky 
The Need for AI-Powered Quick Research
AI-Powered Quick Research gives life sciences teams a faster way to explore and synthesize evidence while maintaining structure, traceability, and scientific rigor. Traditional systematic literature reviews (SLRs) remain the gold standard for high-stakes evidence in life sciences. However, they often take a long time, consume substantial resources, and struggle to keep pace with the volume of publications and the speed of regulatory or commercial decisions.
Teams in HEOR, Medical Affairs, Market Access, and regulatory affairs need a practical bridge: rapid, structured insights that remain transparent, reproducible, and ready to feed into full SLR or submission workflows. AI-powered Quick Research addresses this gap by combining conversational exploration, structured PI(E)COS (Population, Intervention/Exposure, Comparison, Outcome, Study Design) driven synthesis, and full traceability. The result is scalable evidence generation that supports defensible decisions without discarding scientific rigor.
Quick Research Beyond the Full SLR
The assessment landscape is becoming increasingly time-sensitive following the implementation of the EU Health Technology Assessment Regulation (HTAR), effective 12 January 2025. The regulation marks the start of EU-level joint clinical assessments, initially covering centrally authorized oncology medicines and advanced therapy medicinal products (ATMPs). Member state HTA bodies submit PI(E)COS questions into a consolidated joint scope, so one clinical evidence package now has to answer several population and comparator variants against a single deadline.
Recurring deliverables add the second layer of pressure. Clinical evaluation reports, periodic safety update reports, and value dossiers each require a current, reproducible, documented search, and each returns on a fixed cycle through evidence synthesis and literature review workflows.
The full SLR remains the correct instrument for submission-grade evidence. It is the wrong instrument for a Thursday question. When no governed option exists for the fast question, teams route it to an untracked tool, and the answer cannot be reused, defended, or carried forward.
What AI-Powered Quick Research Actually Does
Quick Research offers graded entry points into the same provenance chain, so the speed of the route does not determine whether the output is traceable.
AI-Powered Quick Research Workflow
Upload-Based Synthesis from Full Texts
You upload full-text PDFs, define the study objective and PI(E)COS criteria, and generate an evidence synthesis without running search, deduplication, or screening. A general summary requires at least two uploaded articles. This path suits a focused comparison or a check on an evidence set you already hold.
Conversational Abstract Research
You open a session and build context across several turns, discussing population, intervention, outcomes, and objectives before requesting a summary. The platform generates a narrative summary referencing life sciences sources, with PubMed identifier links in the text and a source reference tray listing title, PubMed ID, publication year, authors, and a route to the original record. Source references persist when you continue the conversation or resubmit a prompt.
Deep Search for Structured Evidence
When the question warrants more rigor, Deep Search runs the full sequence. It generates narrow, broad, and primary or secondary objectives for you to choose between, then builds the PI(E)COS framework, inclusion and exclusion criteria, and the search query before retrieving articles, removing duplicates, screening, and producing an evidence summary.
Each step is editable before the workflow continues. After generation, you can download the summary in TXT or DOCX, view the references, view the executed query, view the criteria, save the summary, or move the package into a literature review project with the objective, query, and criteria already populated. Final query results export to a spreadsheet containing PubMed ID, article title, and article URL, which lets an information specialist review the strategy independently.
Collaboration and Session Control
Project owners add collaborators as Viewer or Editor. Viewers receive in-app and email notifications with a direct link and can view and download, but cannot edit. Cloning a project copies its PDFs, PI(E)COS, objective, and evidence synthesis while excluding collaborators, so a variant never disturbs the original. Sessions and summaries can be renamed or deleted with confirmation, and shared work appears in dedicated Shared Summaries and Shared Sessions views.
Long-running Deep Search and standard queries continue in the background while you work elsewhere, with the live progress indicator restored on return. That removes the practical incentive to abandon the structured path for a faster, undocumented one.
What Makes AI-Powered Quick Research Defensible
| Capability | What it Delivers | The Review Impact |
|---|---|---|
| Structured objective and PI(E)COS | A versioned definition governing everything downstream | Criteria that exist only inside a prompt cannot be audited |
| Editable inclusion and exclusion criteria | A criteria set with a change history | Reviewers can show what was applied and when it changed |
| Exportable search query and result list | Query string, database, date, and record identifiers | An information specialist can check the strategy independently |
| Source-linked synthesis | Summary statements bound to PubMed records | Verification becomes a click rather than a search |
| Move to literature review | Objective, query, and criteria carried forward | Scoping work becomes the start of a formal review, not a discard |
| Role-based sharing | Defined viewer and editor permissions | One authoritative version instead of emailed copies |
What Published Evaluations Show
Efficiency claims are only interpretable next to a recall target, and published results vary widely by corpus and review type.
An evaluation of prioritization across ten completed reviews reported a median reduction in screening burden of 47.1% at a true recall of 95% (Hamel et al., 2020). A purpose-built classifier for identifying randomized controlled trials reported average labor time savings of 45.4% under assisted double screening, with recall of 0.998 compared with 0.919 for a single human reviewer, rising to 74.4% savings under a stepwise design (Research Synthesis Methods, 2024). A synthesis of natural language processing approaches places reported workload reduction between 13% and 96%, clustering between 30% and 60%, with most studies exceeding 90% recall (PMC, 2026).
MadeAi tested its own screening performance against a human-adjudicated set of 2,302 records for a Class II medical device using the ISPOR Elevate-GenAI framework, reporting inclusion recall of 83% with precision of 80.7%, exclusion recall and precision of 96%, overall article relevance of 93%, and 84% concordance between model reasoning and reviewer rationale (MadeAi, ISPOR 2026 interim evaluation). Treat that result, and every vendor-reported figure including this one, as a different evidence tier from independent peer-reviewed evaluation.
Two rules follow. Set the acceptable recall target before configuring anything, and validate performance on your own corpus rather than assuming a published benchmark transfers.
Literature Review: A Comparison
| Dimension | Traditional SLR | Ungoverned General-Purpose LLM | AI-Powered Quick Research |
|---|---|---|---|
| Time to first insight | Weeks to months | Minutes, quality unverified | Minutes to hours |
| Structure applied | Protocol and dual review | Prompt dependent | PI(E)COS, editable criteria and query |
| Traceability | High, reviewer maintained | Typically none | Source-linked records and stored query |
| Escalation path | Not applicable | Manual rework | Direct transfer into a literature review project |
| Collaboration | Shared drives and email | Single user | Viewer and editor roles on one version |
| Use case | Submission-grade evidence | Not suitable for decisions | Scoping, rapid response, and gap analysis |
Use Cases
- Scoping and gap analysis before committing resources to a full review.
- Medical information responses and internal briefings that need source attribution.
- Early evidence packages supporting joint clinical assessment and value narrative development.
- Clinical evaluation report support, where device literature needs rapid synthesis with traceable sources.
- Cross-functional alignment, with medical, HEOR, and access teams iterating on one shared evidence base.
Best Practices for Implementation
- Refine the objective conversationally before launching Deep Search, since downstream quality depends on it.
- Edit every generated PI(E)COS element, criterion, and query, and treat the generated version as a draft.
- Set the recall target for the question type before configuring screening.
- Require named reviewer sign-off on any summary that informs an external decision.
- Use the move to literature review pathway as soon as a topic proves strategically important.
- Record tool, version, stage, and oversight design while you work rather than reconstructing it later.
- Agree naming conventions, retention rules, and role assignments before the first project.
When Not to Use Quick Research
High recall frequently arrives with low precision, which moves effort into full-text review rather than removing it. Performance also fails to transfer between corpora, so a tool that performs well on a drug intervention question may underperform on a device or qualitative question with heterogeneous terminology.
Grounding a summary in retrieved literature reduces fabrication risk without eliminating it. Source display is what makes the residual risk manageable, because an unverifiable sentence and a supported one read identically.
Accountability does not move to the tool. Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence have confirmed that evidence synthesists remain responsible for their synthesis, that human oversight is required, and that any AI use which makes or suggests judgments must be reported transparently (joint position statement, 2025).
Do not use a rapid pathway as the evidence base for a pivotal submission, a published systematic review, or any deliverable requiring dual independent screening and a PRISMA 2020 flow from protocol onward. Coverage is also limited to supported life sciences databases, so grey literature and non-English sources need supplementary searching.
Quick Research – The Future
Guidance is consolidating rather than fragmenting. The US Food and Drug Administration issued draft guidance in January 2025 proposing a risk-based credibility assessment framework tied to a defined context of use (Federal Register notice), and structured evaluation frameworks are starting to standardize how tool performance gets reported.
Two consequences follow for buyers. Published validation will displace marketing performance figures as the basis for procurement, and living surveillance will become routine for high-value indications once objectives, criteria, and queries persist as reusable objects.
Conclusion
Quick Research does not replace the systematic literature review. It gives the fast question a governed home, so that scoping work produces artifacts instead of a discarded chat log, and so that the decision to escalate is based on evidence rather than instinct.
The useful next step is small. Take one real scoping question you would otherwise answer informally, run it through a structured pathway, and compare the time taken and the defensibility of the result against your current method. Our guide to the AI-assisted SLR process covers the full lifecycle for teams deciding where the boundary between rapid and systematic should sit.
Author’s Note: This article was supported by AI-based research and writing, with Claude 5 assisting in the creation of text and images.
FAQs
What is AI-powered quick research and how does it differ from a full SLR?
It is a structured evidence synthesis pathway that produces PI(E)COS-aligned summaries from uploaded full texts or from a conversational search session, in minutes to hours. A full SLR is the protocol-to-PRISMA process that supports submission-grade claims. Quick Research accelerates discovery and produces artifacts that transfer into that process.
How does Quick Research keep AI output traceable?
Every summary carries source references with PubMed identifiers and links. Objectives, PI(E)COS criteria, and search queries are editable and retained; the executed query and result list can be exported, and the whole package can move into a literature review project with its context intact.
Can teams collaborate on a Quick Research project?
Yes. Owners add collaborators as Viewer or Editor. Viewers are notified with a direct link and can view and download but not edit. Cloning a project preserves the PDFs, criteria, objective, and synthesis while excluding collaborators.
How much time does AI-assisted screening actually save?
Published evaluations report reductions between 13% and 96%, most commonly between 30% and 60%, with most studies retaining recall above 90%. One prioritization study reported a median 47.1% reduction at 95% true recall. The figure that applies to your review depends on corpus, review type, and the recall target you set.
Does using AI in evidence synthesis need to be disclosed?
Yes. The joint position from Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence states that any use of AI or automation that makes or suggests judgments should be fully and transparently reported, with human oversight applied throughout.
When should a team not use a rapid research pathway?
Avoid it as the evidence base for pivotal submissions, published systematic reviews, or any deliverable requiring dual independent screening and full PRISMA reporting. Use it for scoping, gap analysis, rapid response, and preparing the objective and criteria for a formal review.
