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

PURPOSE The rapid expansion of scientific literature has made it increasingly challenging for clinicians and researchers to efficiently identify relevant evidence. While large language models (LLMs) offer promising solutions for automating literature review tasks, few tools support integrated workflows that enable trend analysis as well. This study aimed to develop and evaluate Rapid Clinical Evidence eXplorer ( RaCE-X ), a Generative Pre-trained Transformer (GPT)–based automated pipeline designed to streamline abstract screening, extract structured information, and visualize key trends in clinical research. METHODS We used GPT-4.1 mini to screen 865 PubMed abstracts based on predefined screening criteria. Structured information was then extracted from the 87 relevant abstracts based on a predefined information model covering nine fields. A gold standard data set was created through expert review to assess model performance. The extracted information was visualized through an interactive dashboard. Usability was evaluated using the Post-Study System Usability Questionnaire (PSSUQ) and open-ended feedback from five clinical research coordinators. RESULTS RaCE-X demonstrated high screening performance (precision = 0.954, recall = 0.988, F1 = 0.971) and achieved strong average performance in information extraction (precision = 0.977, recall = 0.989, F1 = 0.983), with no hallucinations identified. Usability testing indicated generally positive feedback (overall PSSUQ score = 2.8), with users noting that RaCE-X was intuitive and effective for data interpretation. CONCLUSION RaCE-X enables efficient GPT-based abstract screening, structured information extraction, and research trend exploration, thereby facilitating the summary of clinically relevant evidence from the biomedical literature. This study demonstrates the feasibility of using LLMs to reduce manual workload and accelerate evidence-based research practices.