Diagnostic accuracy of AI in chest radiography for pneumonia and lung cancer: A meta-analysis.

Rationale and Objectives: Chest radiography (CXR) is the most common imaging test worldwide for evaluating pulmonary disease, yet its sensitivity for pneumonia and lung cancer is limited. Artificial intelligence (AI) based image analysis has shown promise to aid radiographic diagnosis. A meta-analysis was performed for AI algorithms evaluation for detecting pneumonia or lung nodules on CXR, and AI performance was compared to human readers. Materials and Methods: Following PRISMA guidelines, searched PubMed/Medline, Embase, Cochrane, and IEEE Xplore (Jan 2017-July 2025) for diagnostic accuracy studies of AI on CXR. Eligible studies included any prospective or retrospective design reporting sensitivity and specificity for AI-based pneumonia or lung nodule detection, with an independent reference standard. Data were extracted using a standardized form and quality was assessed with QUADAS2. Results: Fifteen studies (≈12,000 CXRs) met inclusion criteria; individual AI algorithms achieved sensitivities of ∼70-97 % and specificities of ∼85-95 %. Meta-analysis yielded a pooled sensitivity of 88 % and specificity of 90 % for AI pneumonia detection. For lung nodules, pooled AI sensitivity was ≈ 72 % and specificity ≈ 95 %. One representative deep-learning model for detecting nodules. AI tended to miss very small or central nodules but detected ∼90 % of larger nodules. Crucially, using AI as a second reader improved radiologist performance, increasing sensitivity by approximately 9-10 %age points. Conclusion: AI algorithms demonstrate high diagnostic accuracy for pneumonia on CXR and can markedly increase the detection of occult lung nodules when used as a second reader. However, performance varies by lesion characteristics. Overall, AI has strong potential to enhance clinical chest radiograph interpretation.