Artificial Intelligence to Automate Network Meta-Analyses: Four Case Studies to Evaluate the Potential Application of Large Language Models
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View abstract on PubMed
Summary
This summary is machine-generated.This study shows that large-language models (LLMs) can automate data extraction and R script generation for network meta-analyses (NMAs), significantly saving time and reducing errors in systematic reviews.
Area Of Science
- Artificial Intelligence in Medical Research
- Computational Statistics
- Evidence Synthesis
Background
- Artificial intelligence (AI) offers potential to revolutionize systematic reviews and network meta-analyses (NMAs).
- Large-language models (LLMs) demonstrate human-level performance on various tasks.
- This pilot study explores LLM application in automating key NMA development steps.
Purpose Of The Study
- To assess the feasibility of using Generative Pre-trained Transformer 4 (GPT-4) for automated data extraction.
- To evaluate LLM's capability in generating R scripts for conducting NMAs.
- To determine if LLMs can interpret NMA results accurately.
Main Methods
- Four case studies with binary and time-to-event outcomes were analyzed.
- A Python script facilitated communication with the LLM via API calls.
- The LLM was prompted for data extraction, R script creation, and analysis reporting.
Main Results
- The LLM achieved over 99% accuracy in data extraction across multiple runs.
- Generated R scripts were executable end-to-end without manual intervention.
- The LLM produced high-quality reports with accurate analysis descriptions and interpretations.
Conclusions
- Current LLMs show promise for automating data extraction, code generation, and NMA interpretation.
- Significant time savings and reduced human error are potential benefits.
- Routine technical checks are essential, and LLM consistency is expected to improve.
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