Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
arXiv:2606.19345v1 Announce Type: new Abstract: The rapid increase in scientific publications leads to the fact that manual study screening in systematic literature reviews (SLRs) is increasingly resource consuming, inefficient, and inconsistent. Classifying studies that clearly report health-related quality-of-life results, such as EQ-5D data, requires a high level of clinical interpretation and poses challenges for human reviewers. This study investigates the use of Google's Gemini and Gemma large language models (LLMs) in automating EQ-5D detection in the PubMed biomedical database based on
The proliferation of scientific literature and the advancement of large language models are converging to necessitate and enable automated review processes.
Automating literature reviews with AI can significantly accelerate scientific progress and drug discovery by making research more efficient and consistent.
The manual, labor-intensive process of systematic literature reviews will increasingly be augmented or replaced by AI, improving speed and accuracy.
- · Academic researchers
- · Pharmaceutical companies
- · AI developers
- · Healthcare sector
- · Manual literature review services
- · Researchers without AI tools
Scientific literature screening becomes substantially more efficient and consistent.
Faster identification of relevant studies leads to quicker insights and potentially accelerated drug development.
The enhanced speed and scale of AI-driven literature review could lead to the discovery of previously overlooked connections within vast amounts of research data, fostering new scientific breakthroughs.
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