Randomized trial of AI-assisted chest radiograph triage reducing time-to-antibiotics in suspected pneumonia
Abstract
Background: Pneumonia remains a major contributor to emergency department morbidity, and timely administration of antibiotics is a key determinant of outcomes. Conventional radiograph workflows often delay interpretation during high-volume periods, prolonging clinical decision-making. Recent advances in artificial intelligence have enabled automated prioritization of imaging studies, yet evidence from randomized trials assessing its real-time clinical impact remains limited.
Objective: To evaluate whether AI-assisted triage of chest radiographs in the emergency department reduces time-to-antibiotics and improves early clinical outcomes in patients with suspected pneumonia compared with standard workflow.
Methods: A randomized controlled trial was conducted over a six-month period, enrolling adult patients presenting with symptoms suggestive of pneumonia who received chest radiographs as part of routine evaluation. Participants were randomized in a 1:1 ratio to AI-assisted triage or standard radiograph workflow. The AI system automatically flagged radiographs with suspected infiltrates, prioritizing them for expedited radiologist review. Data collected included baseline demographics, time-to-antibiotics, radiologist report turnaround time, length of stay in the emergency department, and early clinical response at 48 hours using a standardized ordinal scale. Statistical analyses were performed using independent t-tests and chi-square tests, with significance set at p < 0.05.…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design. Reported size: 140 participants or records.
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