Collaborative large language models (LLMs) are all you need for screening in systematic reviews.

Area of Science:

• Artificial Intelligence in Medical Research
• Natural Language Processing for Evidence Synthesis

Background:

• Systematic reviews (SRs) require rigorous study screening, a process often bottlenecked by manual effort.
• The potential of large language models (LLMs) to automate and enhance SR screening remains largely unexplored.

Purpose of the Study:

• To evaluate the effectiveness of LLMs in automating the screening process for systematic reviews.
• To compare the performance of individual LLMs versus collaborative LLM approaches.

Main Methods:

• An observational study using labeled data (titles and abstracts) from five SRs.
• Individual LLMs (GPT-4, Claude-3-Sonnet, Gemini-Pro-1.0) and collaborative LLM strategies were employed.
• Performance metrics included accuracy, precision for exclusion, recall for inclusion, and work saved over samples (WSS).

Main Results:

• Individual LLMs demonstrated high precision for exclusion (up to 99.7%) and recall for inclusion (up to 96.6%).
• Collaborative LLM approaches, particularly using GPT-4 and Claude-3S, achieved superior average precision (99.9%) and recall (98.5%).
• Collaborative LLMs resulted in an average WSS of 63.5%, significantly higher than individual models (45.2%).

Conclusions:

• Collaborative LLMs offer an efficient and high-performing solution for systematic review screening.
• This automation supports the timely and continuous updating of evidence synthesis.
• Future research should explore LLM capabilities across diverse datasets and proprietary models.