AI-Powered Worklist Tool Reduces Radiology Reporting Times
A new study published in the Journal of Medical Internet Research reveals that an AI-powered worklist triage and report generation tool reduced reporting times by more than 70 percent in hospital settings.
An AI-based worklist triage and reporting tool significantly shortens report generation and finalization times in hospital radiology departments while enabling sorting based on clinical priority.
Challenges of Traditional Workflows
The first-in, first-out method commonly used in radiology departments ensures that examinations are interpreted in order, but it may not always account for clinical urgency. This can lead to delays in reporting processes for patients requiring urgent attention.
Research and Scope of the Study
Dr. Srinath Sridharan and colleagues from Changi General Hospital in Singapore conducted a prospective, single-center crossover study to evaluate the operational impact of AI. As part of the research, eight board-certified radiologists reviewed 1,054 chest radiographs obtained between November 2023 and January 2024.
Significant Reduction in Times
Thanks to the AI-powered workflow, median report generation times dropped from 2 minutes to 0.53 minutes, a decrease of 73.3 percent. For normal chest radiographs, this rate fell by up to 90 percent, dropping from 2 minutes to 0.2 minutes. Average turnaround times decreased from approximately 15 hours to 82 minutes.
Clinical Priority and Future Perspective
The research findings indicate that AI systems can play a crucial role not only in image analysis, but also in organizing the reading queue according to clinical urgency and in report generation. This situation offers advantages to patients and healthcare providers in busy healthcare institutions.