AI-Powered Chest X-ray Triage: Improving Efficiency & Accuracy
Care Process & Redesign
Technology
National Healthcare Innovation and Productivity Medals
Agency for Integrated Care - Intermediate and Long Term Care
26 December 2025
Optimize radiologist efficiency by reducing manual triage workload. AI-driven triage streamlines workflows, improving efficiency.
Year Submitted: 2025
Published Date: 26 December 2025
Tags: Care Process ＆ Redesign, Access To Care, Turnaround Time, Waiting Time, Productivity, Time Saving, Quality Improvement, Workflow Redesign, Technology, Digital Health, Chat Bots, Digitalisation, Automation, Artificial Intelligence
About this Content
Aims
Optimize radiologist efficiency by reducing manual triage workload. Improve patient outcomes through faster CXR reporting. Ensure high AI diagnostic accuracy while maintaining workflow integration.
Background
Manual CXR triage causes delays, increasing patient risk and radiologist workload. Existing workflows lack AI-driven prioritization, leading to inconsistencies in case urgency detection. Need for an AI-based solution to automatically sort normal, non-urgent, and urgent cases.
Methods
AI-powered CXR triage aims to streamline radiology workflows and improve urgent case prioritization. Lunit INSIGHT CXR automates triage, reducing turnaround time and enhancing patient care.
Results
1.75 FTE saved annually. Before AI: ~6 hours to report urgent cases. After AI: ~3 hours. AI triage prioritizes critical cases instantly, cutting reporting time by half. Faster diagnosis = quicker treatment & improved patient outcomes.
Conclusion
AI-driven triage streamlines workflows, improving efficiency. Faster reporting leads to quicker clinical decisions & better care. Scalability ensures long-term sustainability in radiology innovation.
Lessons Learnt
Manual triage inefficiencies delayed urgent case reporting. High radiologist workload led to bottlenecks in diagnosis. Ensuring AI accuracy & integration within existing workflows.
Additional Information
This project has won National Healthcare Innovation and Productivity Awards 2025 under Best Practice (Automation, IT and Robotics Innovation) category.
Keywords
Artificial Intelligence, Triage, Radiology, Workflow, Accuracy, X-Ray, Chest X-Ray, CXR
Innovators' Details
Innovators' Details
Healthcare Cluster(s) | Agency for Integrated Care - Intermediate and Long Term Care |
Organization(s) Involved | St. Andrew Community Hospital |
Platform(s) | National Healthcare Innovation and Productivity Medals |
Healthcare Professional Group(s) | Healthcare Administration, Medical |
Applicable Specialty or Discipline | Healthcare Administrators, Clinical Research, General Research, InfoTech, Medical, Radiology |
Project Lead(s) | Charlene Liew Jin Yee |
Project Member(s) | Srinath Sridharan |
Connect with this contributor!
Charlene Liew Jin Yee - charlene.liew.j.y@singhealth.com.sg
Srinath Sridharan - sridharan.s@singhealth.com.sg
Project Attachment
1060_CGH_NHIP_2025_AI_Powered_Chest_X_ray_Triage_Improving_Efficiency_Accuracy.pdf