553: NeuroBriefer: A Personalized Platform to Navigate the Expanding Pain Literature

Topic: Education, Societal Impact, and Others
Subtopic: Artificial Intelligence/Machine Learning

Background and Aims: The volume of research relevant to pain has increased rapidly in recent years, spanning basic neuroscience, preclinical models, and clinical studies. While this growth reflects major advances, it also creates a substantial challenge for researchers and clinicians attempting to remain current with the literature (Landhuis, 2016). Here, we aimed to quantify the scale and variability of newly published neuroscience research relevant to pain and to develop a system to support efficient discovery and evaluation of new studies. Our goal was to address the growing burden of literature surveillance while enabling more structured and personalized engagement with the rapidly expanding evidence base.

Methods: We developed an automated ingestion pipeline that continuously scans 412 PubMed-indexed neuroscience journals ranked Q3 or higher, cataloguing newly published articles on a weekly basis. Across a 6-month period, articles were aggregated and analyzed to estimate weekly publication volume and the proportion relevant to pain using a keyword-based classification approach. Building on this pipeline, we developed a personalized literature discovery platform (NeuroBriefer) that uses AI-based methods to rank articles based on user-defined research interests and generate structured summaries capturing the research question, approach, key findings, and interpretation. The platform delivers weekly email digests and provides a web-based interface for browsing, organizing, and sharing collections of relevant papers.

Results: Across 6 months, approximately 38,000 neuroscience articles were catalogued, averaging 1,467 per week. A subset of ~76 articles per week (5.2%) was identified as pain-related, corresponding to ~2,000 new pain-related publications over this period. Weekly publication volume showed notable variability, reflecting fluctuations in journal output and research activity across subfields. These findings highlight both the scale and unpredictability of the literature landscape. Early adoption by approximately 300 researchers across multiple institutions suggests strong demand for tools that reduce the cognitive burden of literature monitoring.

Conclusions: These findings highlight the scale and sustained influx of new research relevant to pain, and the limitations of traditional approaches to staying current. By combining automated ingestion, relevance-based ranking, and structured summarization, NeuroBriefer provides a practical and scalable approach to navigating this expanding literature. The platform is designed to reduce information overload while supporting more efficient and structured engagement with new findings. Tools of this kind may improve knowledge discovery, enhance cross-disciplinary synthesis, and support more effective translation across preclinical and clinical pain research. More broadly, this work demonstrates how AI-driven approaches can be applied in a targeted way to address a clear bottleneck in scientific workflows.

References: Landhuis E. Scientific literature: Information overload. Nature. 2016 Jul 21;535(7612):457-8. doi: 10.1038/nj7612-457a. PMID: 27453968.

Presenting Author: Damien Boorman
Submitting Author: Damien Boorman

Senior Author: Damien Boorman

IASP 2026 Global Year theme of Neuropathic Pain: No