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Rapid Clinical Evidence Explorer: A Generative Pre-Trained Transformer-Powered Tool for Automated Oncology Evidence

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Summary
This summary is machine-generated.

Rapid Clinical Evidence eXplorer (RaCE-X) uses Generative Pre-trained Transformer (GPT) to automate literature review, improving evidence-based research. This tool efficiently screens abstracts, extracts data, and visualizes trends, reducing manual workload for clinicians and researchers.

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Research Informatics

Background:

  • The exponential growth of scientific literature presents significant challenges for evidence synthesis in clinical practice and research.
  • Existing tools for literature review often lack integrated capabilities for trend analysis, necessitating manual workflows.
  • Large language models (LLMs) show potential for automating literature review but require specialized applications for clinical evidence exploration.

Purpose of the Study:

  • To develop and evaluate Rapid Clinical Evidence eXplorer (RaCE-X), an automated pipeline utilizing Generative Pre-trained Transformer (GPT) technology.
  • To streamline the process of abstract screening, structured information extraction, and visualization of research trends in clinical literature.
  • To assess the performance and usability of RaCE-X in facilitating efficient access to clinically relevant evidence.

Main Methods:

  • GPT-4.1 mini was employed for screening 865 PubMed abstracts against predefined criteria, identifying 87 relevant articles.
  • Structured information extraction was performed on relevant abstracts using a nine-field information model, with a gold standard dataset for performance assessment.
  • Usability was evaluated via the Post-Study System Usability Questionnaire (PSSUQ) and qualitative feedback from clinical research coordinators.

Main Results:

  • RaCE-X achieved high performance in abstract screening (F1 = 0.971) and information extraction (F1 = 0.983), with no identified hallucinations.
  • Usability testing yielded positive user feedback, indicated by a low overall PSSUQ score of 2.8, with users finding the tool intuitive.
  • The interactive dashboard effectively visualized extracted information, aiding in the interpretation of research trends.

Conclusions:

  • RaCE-X offers an efficient GPT-based solution for abstract screening, data extraction, and trend analysis, accelerating the synthesis of biomedical evidence.
  • The study confirms the feasibility of leveraging LLMs to significantly reduce manual effort in evidence-based research.
  • RaCE-X supports enhanced evidence-based practice by facilitating quicker identification and summarization of relevant clinical research findings.