How to Conduct a Systematic Literature Review with AI in 2026: PRISMA Guidelines, Citation Tracking & Verifiable PDF Transcripts

1. PRISMA 2026: The New Standards for AI-Assisted Systematic Reviews

Systematic literature reviews represent the highest tier of empirical evidence in academic scholarship. With over 3 million research papers published annually, researchers must combine structured protocols (PRISMA statement) with machine learning tools to screen abstracts, extract quantitative metrics, and synthesize findings without selection bias.

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2. The 4-Phase AI Literature Review Workflow

[1. Identification] ──▶ [2. Screening] ──▶ [3. Eligibility] ──▶ [4. Inclusion & Archive]
 (Elicit / PubMed)       (Title/Abstract)     (Full-Text Audit)    (Neo Cortex PDF Appendix)

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3. Screening Inclusion & Exclusion Criteria with LLMs

Define explicit prompt rubrics when reviewing candidate studies:

• Sample size ($N > 100$)
• Randomized controlled trial or quasi-experimental design
• Peer-reviewed DOI publication within 2020–2026

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4. Archiving Search Protocols with Neo Cortex Exporter

To ensure journal editors can audit your screening process, archive every prompt chain and summary matrix into a formatted PDF using the Neo Cortex AI Chat Exporter.

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5. Synthesizing Interdisciplinary Evidence Tables

Export synthesis tables directly into Markdown or LaTeX for inclusion in your manuscript submission.

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6. Systematic Review FAQs

Q1: Will peer reviewers accept AI screening in a systematic review?

Yes, provided the exact prompt queries and inclusion criteria transcripts are archived and submitted as supplementary material.