# Automated title and abstract screening for scoping reviews using the GPT-4 Large Language Model

arXiv: https://arxiv.org/abs/2311.07918 · HTML: https://mlsanity.com/paper/2311.07918/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Automated Paper Screening for Clinical Reviews Using Large Language Models](https://mlsanity.com/paper/2305.00844/) (2023)
- [Assessing the Ability of ChatGPT to Screen Articles for Systematic Reviews](https://mlsanity.com/paper/2307.06464/) (2023)

## Built on by (most cited first, 6 of 6)

- [The Promise and Challenges of Using LLMs to Accelerate the Screening Process of Systematic Reviews](https://mlsanity.com/paper/2404.15667/) (2024)
- [LLM4SCREENLIT: Recommendations on Assessing the Performance of Large Language Models for Screening Literature in Systematic Reviews](https://mlsanity.com/paper/2511.12635/) (2025)
- [Measuring Risk of Bias in Biomedical Reports: The RoBBR Benchmark](https://mlsanity.com/paper/2411.18831/) (2024)
- [Leveraging LLMs for Title and Abstract Screening for Systematic Review: A Cost-Effective Dynamic Few-Shot Learning Approach](https://mlsanity.com/paper/2512.11261/) (2025)
- [Preliminary Guidelines for Using and Evaluating GenAI Tools to Support Systematic Literature Reviews](https://mlsanity.com/paper/2607.24991/) (2026)
- [Evaluating human and LLM screening workflows in a conceptually complex scoping review: Recall--workload trade-offs and run-to-run consistency](https://mlsanity.com/paper/2608.26885/) (2026)

## Abstract (arXiv)

Scoping reviews, a type of literature review, require intensive human effort to screen large numbers of scholarly sources for their relevance to the review objectives. This manuscript introduces GPTscreenR, a package for the R statistical programming language that uses the GPT-4 Large Language Model (LLM) to automatically screen sources. The package makes use of the chain-of-thought technique with the goal of maximising performance on complex screening tasks. In validation against consensus human reviewer decisions, GPTscreenR performed similarly to an alternative zero-shot technique, with a sensitivity of 71%, specificity of 89%, and overall accuracy of 84%. Neither method achieved perfect accuracy nor human levels of intraobserver agreement. GPTscreenR demonstrates the potential for LLMs to support scholarly work and provides a user-friendly software framework that can be integrated into existing review processes.