Large language models (LLMs) such as ChatGPT have become widely applied in the field of
medical research. In the process of conducting systematic reviews, similar tools can be used …

Systematic reviews (SRs) provide valuable evidence for guiding new research directions.
However, the manual effort involved in selecting articles for inclusion in an SR is error-prone …

Abstract Background Large language models (LLMs) that can efficiently screen and identify
studies meeting specific criteria would streamline literature reviews. Additionally, those …

Systematic reviews are crucial for evidence-based medicine as they comprehensively
analyse published research findings on specific questions. Conducting such reviews is often …

Systematic reviews are vital for guiding practice, research, and policy, yet they are often slow
and labour-intensive. Large language models (LLMs) could offer a way to speed up and …

Insufficient data volume and quality are particularly pressing challenges in the adoption of
modern subsymbolic AI. To alleviate these challenges, AI simulation recommends …

Synthesizing clinical evidence largely relies on systematic reviews of clinical trials and
retrospective analyses from medical literature. However, the rapid expansion of publications …

Systematic evidence syntheses (systematic reviews and maps) summarize knowledge and
are used to support decisions and policies in a variety of applied fields, from medicine and …

Model-driven engineering (MDE) simplifies software development through abstraction, yet
challenges such as time constraints, incomplete domain understanding, and adherence to …

Objective: This paper aims to address the challenges in citation screening (aka abstract
screening) within Systematic Reviews (SR) by leveraging the zero-shot capabilities of large …