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

Abstract

PURPOSE: ), 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.

Ähnliche Arbeiten