AI- Assisted Chest X-Ray Interpretation in Resource-Limited Settings: LuAna Stepped-Wedge Trial Protocol (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Artificial intelligence (AI) has the potential to transform chest radiography (CXR) interpretation by enhancing diagnostic accuracy, identifying subtle findings, reducing errors, and helping prioritize patient care. Although CXR remains a cost-effective and widely used imaging tool, its effectiveness is limited by overlapping anatomy and variability in clinical expertise. Integrating AI can help overcome some of these challenges, especially in resource-constrained settings. However, robust validation in real-world clinical contexts is essential before widespread implementation. This study protocol evaluates whether AI assistance improves general practitioners' ability to detect radiographic findings on CXR in adults with respiratory complaints or undergoing treatment for respiratory diseases, compared to unaided interpretation. Potential benefits include increased diagnostic safety, higher physician confidence, more efficient workflows, and expanded access to expert support in underserved areas. </sec> <sec> <title>OBJECTIVE</title> This project aims to evaluate whether AI assistance enhances physicians’ ability to detect key radiographic abnormalities— including consolidation or pulmonary opacity, pneumothorax, atelectasis, pleural effusion, and cardiomegaly. The primary outcome is the difference in physicians’ diagnostic accuracy (per examination) when assisted by the AI tool compared with usual practice, using the expert radiologist consensus as the reference value. </sec> <sec> <title>METHODS</title> This is a protocol for a multicenter, stepped-wedge, cluster-randomized clinical trial following the CONSORT-AI extension and SPIRIT-AI guidelines. The intervention involves the diagnostic support Solution for CXR - Lung Analysis (LuAna), an AI-powered chest X-ray interpretation tool developed in partnership with the Brazilian Ministry of Health. Across nine cities in Brazil, clusters will transition monthly from unaided chest X-ray interpretation by general practitioners to AI-assisted interpretation, with performance benchmarked against thoracic radiologists. The stepped-wedge design ensures all clusters receive the intervention, reflecting real-world coordination, enhancing acceptability, improving power, and strengthening causal inference through repeated measures. Diagnostic performance will be compared to a reference standard established by thoracic radiologists. </sec> <sec> <title>RESULTS</title> Thirteen research centers across Brazil will participate, covering all five regions and diverse healthcare settings, from primary care to specialized tuberculosis centers. Next steps involve finalizing regulatory approvals and starting participant enrolment once all sites are fully activated. </sec> <sec> <title>CONCLUSIONS</title> This intervention is expected to enhance clinical decision-making by supporting earlier treatment initiation and more appropriate diagnostic pathways for patients with respiratory symptoms, while maintaining a favorable safety profile and high physician usability. The findings from this trial will provide real-world evidence on the clinical utility of AI-assisted chest radiography. If effective, LuAna may leverage its scalability and equity advantages to become a replicable model for integrating AI into routine imaging workflows worldwide, especially in regions with limited access to specialist care. </sec> <sec> <title>CLINICALTRIAL</title> NCT06686251, Registered on 2024-11-13. </sec>

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