Lesson 352 of 2116
Meta-Analysis Assistance: Where AI Helps And Where It Must Not
Meta-analysis demands precision. AI can accelerate extraction and screening — but the effect-size calculations must stay under human control.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The pipeline and where AI fits
- 2meta-analysis
- 3effect size
- 4data extraction
Concept cluster
Terms to connect while reading
Section 1
The pipeline and where AI fits
Compare the options
| Stage | AI role |
|---|---|
| Title/abstract screening | First-pass filtering against inclusion criteria |
| Full-text screening | Second-pass flagging; human makes final call |
| Data extraction | Structured extraction into a template; human verifies |
| Risk-of-bias assessment | Drafting — NOT final judgment |
| Effect size calculation | Do not delegate to LLM — use dedicated tools |
| Forest plot generation | LLM can write the plotting code; you run it |
| Writing discussion | Drafting, with heavy human editing |
Screening at scale
Title and abstract screening is where AI shines — it can process 5,000 abstracts overnight. Tools like Rayyan and the open-source ASReview integrate LLM classifiers with active learning, so they learn your inclusion patterns. Still, human review of all 'include' decisions remains standard practice.
What you must not hand to an LLM
- Final effect-size calculations — use metafor, RevMan, or equivalent
- Heterogeneity statistics (I², tau²) — use the dedicated tool
- Publication-bias assessments (funnel plots, Egger's test) — dedicated tool
- Final risk-of-bias ratings — human expert judgment, with LLM only as a drafting aid
Key terms in this lesson
The big idea: AI accelerates the front end (screening, extraction) and the back end (plotting, drafting). The statistical core must remain in tools designed for it.
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