RANDOMIZED TRIAL OF AI-ASSISTED CHEST RADIOGRAPH TRIAGE REDUCING TIME-TO-ANTIBIOTICS IN SUSPECTED PNEUMONIA
DOI:
https://doi.org/10.71000/08sayt24Keywords:
Algorithms; Antibiotics; Artificial Intelligence; Clinical Decision-Making; Emergency Service, Hospital; Pneumonia; Radiography, ThoracicAbstract
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.
Results: A total of 140 participants were analyzed. The AI-assisted group demonstrated a significantly shorter time-to-antibiotics compared with the standard workflow group, along with faster radiology reporting times and modest reductions in emergency department length of stay. The proportion of patients demonstrating early clinical improvement at 48 hours was also higher in the AI-assisted arm. No adverse effects related to AI implementation were observed.
Conclusion: AI-assisted triage of chest radiographs meaningfully improved critical process measures and early clinical outcomes in suspected pneumonia, supporting its integration into acute-care imaging workflows.
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Copyright (c) 2025 Ariba Mumtaz, Muhammad Nasser Javaid, Ameet Kumar Lalwani, Shaikh Khalid Muhammad, Muhammad Zakria (Author)
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