Optimizing Chest X-Ray Workflows: The Role of AI as a Support Tool

Abstract

Demand for medical imaging is rising at a faster rate than most other aspects of healthcare and at a speed with which NHS radiology services cannot currently cope. This growing volume has placed considerable strain on radiology services already facing workforce shortages and resource limitations. Consequently, many departments are grappling with significant backlogs and delayed reporting times. NHS England guidance on diagnostic imaging reporting turnaround times (TAT) came into effect on 9th August 2023. The priority TAT is that no examination should take longer than 4 weeks to be reported. Whilst the TATS provide best practice; difficulty remains with how examinations are prioritised. Many institutions still process their examination worklists following the first-in, first-out principle. The ordering physician’s urgency information is often incomplete or presented as ambiguous and ill-defined priority level, such as critical, ASAP, or STAT. Artificial intelligence (AI) has emerged as a potential solution to help address these workload challenges. By triaging radiographs based on urgency, AI tools can assist in streamlining workflows, prioritizing critical findings, and reducing turnaround times. This technological support can enable imaging to focus on more complex cases, optimize reporting efficiency, and ultimately enhance patient care delivery. Importantly, AI’s role should be seen as complementary—enhancing, not replacing, the expertise and clinical judgment of radiology professionals. This presentation will explore the current pressures on UK chest radiograph reporting workloads, how these challenges affect patient care, and the potential role of AI

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