When Is a Rapid Review the Right Call?

Most HEOR and Market Access teams know when they need a full SLR. The harder call is knowing when they don’t. These are the situations where a rapid review tends to make more sense:

Go/no-go decisions: By the time a full Systematic Literature Review (SLR) report is out, the decision it was meant to inform has often already been made on incomplete information, or made late. That mismatch is getting worse, not better: the EU's Joint Clinical Assessment now runs on a single SLR whose search cut-off is tied tightly to the EMA marketing authorization date, and the JCA report is expected to land no later than the day authorization is granted, which pulls the evidence-readiness deadline earlier across the board, not just for the JCA dossier itself.
In-licensing and strategy calls: These need evidence fast enough to sit inside a deal timeline, without the corners being cut in ways that make the evidence indefensible later.
Early development: Identifying unmet need, shaping protocol design, and informing pipeline prioritization are all judgment calls that benefit from a fast evidence base more than an exhaustive one.
Resource allocation and feasibility: Before committing the budget to a full SLR, NMA, or pipeline candidate, a quick, credible read on the existing evidence shows how much exists and where the gaps are, which is what makes that commitment defensible.
Supplementary evidence for economic models: A full SLR is usually reserved for the core clinical efficacy inputs. Rapid methods can cover the secondary parameters, such as costs, resource use, and epidemiology, where the goal is filling gaps in the model rather than exhaustive coverage.

Where Automation Changes the Calculus

It helps to be precise about what a TLR actually trades away. A Systematic Literature Review (SLR) identifies, evaluates, and synthesizes all relevant evidence against a pre-defined protocol, using two independent reviewers, the combination that gives it the lowest bias and the highest reproducibility of any review type. Targeted Literature Reviews (TLRs) narrow the search and typically rely on single-reviewer screening, prioritizing speed over exhaustiveness. This makes them well suited to treatment-landscape questions, epidemiology, or early research inputs rather than a submission-grade evidence base. It's also worth knowing the category itself is younger and less standardized than SLR methodology- "rapid review," “quick scoping review,” “rapid evidence assessment,” "focused systematic review," and "targeted literature review" have been used semi-interchangeably in the literature.

That profile, with a narrower scope, a single reviewer, and a speed-first priority, is a natural fit for automation. It's why an automated Targeted Literature Review (TLR) workflow tends to get used for a specific, recurring set of jobs: gap analyses to flag where evidence is thin, early-stage landscape assessments and evidence scoping, competitive intelligence and benchmarking, and a cost- and time-efficient gut-check before committing to a full SLR.

How the Workflow Actually Runs

Mechanically, it's a five-step handoff rather than a step-by-step manual process including:

Setup: Choose single-AI-reviewer mode, then select which stages the AI runs: title/abstract screening, full-text review, and data extraction. You can also turn on auto-fetch for missing open-access PDFs.
Input: A clear, specific research question. The narrower the question, the better the search and protocol it produces.
Automated Steps: The agent generates the search query and protocol, retrieves records from PubMed and OpenAlex, de-duplicates them, then moves through screening, extraction, and reporting.
Progress: You get an email as each stage completes, and the workflow advances automatically without needing a manual handoff.
Output: A TLR report. For a well-scoped question, this can take about an hour from start to finish, compared with weeks in a typical review cycle.

Results are ready to review as each stage finishes and check what the agent screened, extracted, and reported.

"AI TLR Agent gives you the foundation you can build on rather than waiting for your review to be perfected."

Key Benefits and Why They Hold Up

Speed: This is the easiest claim to make and the one worth checking. An independent benchmarking study ran EasySLR's AI-as-reviewer function against human reviewers on a live economic SLR in breast cancer. The Result: AI matched human reviewers on accuracy for abstract screening and PRISMA generation within the limits of this evaluation, and reduced time on SLR tasks by about 40%. (Source)
Scalability: As more indications, more markets, and more pipeline candidates all need evidence generation work in parallel, and as regulatory timelines compress rather than expand, the constraint shifts from "can we do one review well" to "can we do many reviews running in parallel at once without the quality dropping." That's a throughput problem as much as a speed problem, and it's the harder one to solve manually.
Transparency: This is where the field's attention has moved lately, and for good reason. RAISE (Responsible AI use in Evidence SynthEsis) is a guidance effort now in its third drafted version as of March 2026, built around documenting exactly how AI was used at each review stage. A parallel effort, PRISMA-trAIce (Transparent Reporting of Artificial Intelligence in Comprehensive Evidence Synthesis), was proposed in December 2025 as a discipline-agnostic extension for reporting AI use as a review tool rather than a research subject, though it's worth flagging that the PRISMA Executive has since publicly noted the checklist hasn't gone through their formal endorsement process despite the name, so it's an emerging proposal rather than a settled standard. Either way, the direction is clear: being able to show which model did what, at which stage, is becoming a governance expectation, not a nice-to-have.

Where This Leaves Evidence Teams

None of this replaces the SLR, and it isn't meant to. For the kind of internal, time-boxed decisions covered above, a well-run, transparent TLR is often the right tool for the job, not a lesser one. If you want to see the mechanics in more detail, EasySLR's TLR workflow guide walks through the full setup.

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Sources & further reading

Lumanity: How to Undertake a Literature Review
ISPOR: Definition and Methodology of Rapid Literature Reviews
ISPOR Europe 2026: AI-Enabled Evidence Synthesis for EU Joint Clinical Assessment (JCA)
L.E.K. Consulting: Transitioning to EU-wide Health Technology Assessments: Implications for Pharma
PMLive/Amiculum: Does real-world use of AI in systematic literature reviews result in time and cost savings? (Nov 2025)
RAISE guidance (OSF): Responsible AI use in Evidence SynthEsis
Holst et al.: PRISMA-trAIce Checklist, JMIR AI (2025)

Where Automation Changes the Calculus

How the Workflow Actually Runs

Mechanically, it's a five-step handoff rather than a step-by-step manual process including:

Setup: Choose single-AI-reviewer mode, then select which stages the AI runs: title/abstract screening, full-text review, and data extraction. You can also turn on auto-fetch for missing open-access PDFs.

Input: A clear, specific research question. The narrower the question, the better the search and protocol it produces.

Automated Steps: The agent generates the search query and protocol, retrieves records from PubMed and OpenAlex, de-duplicates them, then moves through screening, extraction, and reporting.

Progress: You get an email as each stage completes, and the workflow advances automatically without needing a manual handoff.

Output: A TLR report. For a well-scoped question, this can take about an hour from start to finish, compared with weeks in a typical review cycle.

Key Benefits and Why They Hold Up

Speed: This is the easiest claim to make and the one worth checking. An independent benchmarking study ran EasySLR's AI-as-reviewer function against human reviewers on a live economic SLR in breast cancer. The Result: AI matched human reviewers on accuracy for abstract screening and PRISMA generation within the limits of this evaluation, and reduced time on SLR tasks by about 40%. (Source)

Scalability: As more indications, more markets, and more pipeline candidates all need evidence generation work in parallel, and as regulatory timelines compress rather than expand, the constraint shifts from "can we do one review well" to "can we do many reviews running in parallel at once without the quality dropping." That's a throughput problem as much as a speed problem, and it's the harder one to solve manually.

Transparency: This is where the field's attention has moved lately, and for good reason. RAISE (Responsible AI use in Evidence SynthEsis) is a guidance effort now in its third drafted version as of March 2026, built around documenting exactly how AI was used at each review stage. A parallel effort, PRISMA-trAIce (Transparent Reporting of Artificial Intelligence in Comprehensive Evidence Synthesis), was proposed in December 2025 as a discipline-agnostic extension for reporting AI use as a review tool rather than a research subject, though it's worth flagging that the PRISMA Executive has since publicly noted the checklist hasn't gone through their formal endorsement process despite the name, so it's an emerging proposal rather than a settled standard. Either way, the direction is clear: being able to show which model did what, at which stage, is becoming a governance expectation, not a nice-to-have.

Where This Leaves Evidence Teams