Pertama Partners

AI Clinical Research and Literature Review Automation

Use AI to accelerate literature searches, summarise clinical studies, synthesise evidence across sources, and support research protocol development aligned with MOH and institutional review board requirements for Southeast Asian healthcare research teams.

HealthcareIntermediateAI Readiness & Strategy3-5 weeks

Transformation

Before & After AI

What this workflow looks like before and after transformation

Before

Literature reviews take weeks to months, with researchers manually searching PubMed, screening hundreds of abstracts, and reading dozens of full papers. Study summaries are inconsistent in format and depth. Evidence synthesis is time-consuming and prone to missing relevant studies. Research protocol drafting starts from scratch each time with limited templates.

After

AI-assisted literature search identifies relevant studies in hours instead of weeks. Abstract screening is 80% automated with researcher oversight. Study summaries follow standardised formats for easy comparison. Evidence synthesis tables are generated automatically with quality assessments. Research protocol templates are pre-populated based on study type and institutional requirements.

Implementation

Step-by-Step Guide

Follow these steps to implement this AI workflow

1

Define Research Questions and Search Strategy

1 week

Formulate structured research questions using PICO(S) framework. Develop comprehensive search strategies for PubMed, Cochrane Library, and regional databases. Define inclusion/exclusion criteria for study selection.

Literature Search Strategy Builder
You are a research librarian. Develop a comprehensive literature search strategy for the following research question: [QUESTION]. Generate search terms, MeSH headings, Boolean operators, and database-specific queries for PubMed, Cochrane, and EMBASE.
Run the generated search strings in each database and record the results count. Refine terms if the yield is too large (more than 2,000 results) or too small (fewer than 50). Document the final strategy for reproducibility in your methods section.
2

Automate Abstract Screening and Study Summarisation

1-2 weeks

Use AI to screen abstracts against inclusion criteria, categorise studies by relevance, and generate structured summaries of included papers. Maintain researcher oversight for borderline decisions.

Clinical Study Summary Generator
You are a clinical research analyst. Summarise the following study in a structured format covering objective, methods, population, intervention, key findings, limitations, and relevance to [RESEARCH QUESTION]. Keep the summary under 300 words.
Process abstracts in batches of 20-30. Have a second researcher verify AI screening decisions for at least 20% of abstracts to calibrate accuracy. Re-check all borderline cases manually.
3

Synthesise Evidence and Generate Comparison Tables

1-2 weeks

Use AI to compile evidence synthesis tables, identify patterns across studies, assess overall evidence quality, and draft narrative synthesis sections. Highlight gaps in the evidence base and areas needing further research.

Evidence Synthesis and Gap Analysis Prompt
You are a systematic review methodologist. Given the following set of study summaries on [TOPIC], synthesise the evidence by identifying consistent findings, conflicting results, evidence quality patterns, and research gaps. Draft a narrative synthesis paragraph.
Review AI-generated synthesis carefully for accuracy. Verify that all cited findings match the source studies. Use the gap analysis to inform your discussion section and future research recommendations.
4

Support Research Protocol Development

1 week

Use AI to draft research protocol sections based on the literature review findings. Pre-populate institutional review board (IRB) submission documents. Ensure alignment with MOH research ethics guidelines and institutional requirements.

Research Protocol Section Drafter
You are a clinical research protocol writer. Draft the [SECTION] of a research protocol for a study on [TOPIC]. Align with MOH research ethics guidelines and IRB submission requirements. Include references to the literature review findings.
Use AI to draft individual protocol sections, then assemble and harmonise the full document. The principal investigator must review and approve all sections. Submit through your institution's ethics committee process.

Get the detailed version - 2x more context, variable explanations, and follow-up prompts

Tools Required

AI language model for text analysis and synthesisLiterature database access (PubMed, Cochrane Library, EMBASE)Reference management software (Zotero, Mendeley, or EndNote)Systematic review management tool (Covidence, Rayyan, or similar)

Expected Outcomes

Reduce literature review completion time from months to 2-3 weeks

Screen 80% of abstracts automatically with researcher oversight on borderline cases

Generate standardised evidence tables covering all included studies consistently

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Common Questions

Yes, provided you transparently disclose AI use in your methods section and maintain researcher oversight throughout. Many journals now accept AI-assisted research with proper disclosure. The key is that researchers validate all AI outputs, make methodological decisions, and take responsibility for the final analysis. Document your AI-assisted workflow for reproducibility.

Studies show AI achieves 85-95% sensitivity in abstract screening (meaning it catches most relevant studies). Specificity is lower (70-85%), meaning it may include some irrelevant studies that humans would exclude. The recommended approach is AI-first screening with human review of borderline cases and a random 20% verification sample. This is faster than fully manual screening while maintaining quality.

AI can introduce bias if the prompts or training data favour certain conclusions. Mitigate this by: providing AI with all studies (not just those supporting your hypothesis), using structured prompts that ask for conflicting findings explicitly, having multiple researchers review the synthesis, and following established systematic review methodology (PRISMA, Cochrane guidelines). AI handles the volume; researchers ensure methodological rigour.

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