Can large language models fully automate or partially assist paper selection in systematic reviews?

ABSTRACT:

Background/aims

Large language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building, has yet to be studied in detail.

Methods

We introduced two LLM-based approaches of systematic review: an LLM-enabled fully automated approach (LLM-FA) utilising three different GPT-4 plugins (Consensus GPT, Scholar GPT and GPT web browsing modes) and an LLM-facilitated semi-automated approach (LLM-SA) using GPT4's Application Programming Interface (API). We benchmarked these approaches using three published systematic reviews that reported the prevalence of diabetic retinopathy across different populations (general population, pregnant women and children).

Resu

SUBMITTER: Chen H 

PROVIDER: S-EPMC12320601 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature