Challenges of Image-Based Diagnosis of Respiratory Diseases with Artificial Intelligence: A Systematic Review and Meta-Analysis
Abstract Objective This article aims to synthesize the diagnostic accuracy of artificial intelligence (AI) for CT-based diagnosis of major respiratory diseases (COVID-19, tuberculosis [TB], COPD/ILA, lung nodules/cancer) between 2020 and 2025, and to identify barriers to clinical adoption spanning data standardization, interpretability, workflow integration, and radiation protection. Materials and Methods Following PRISMA 2020, we searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and screened preprints (January 2020 to September 2025). Eligible human studies reported the diagnostic performance of AI (ML/DL/CNN/CAD) using CT. Primary outcomes were sensitivity, specificity, and AUC; secondary outcomes included CT dose metrics, explainability, and workflow effects. Risk of bias was assessed using QUADAS-2/PROBAST-AI; reporting quality was assessed using CLAIM. Bivariate random-effects meta-analysis yielded pooled estimates with HSROC; heterogeneity ( τ 2 , I 2 ) and publication bias (Deeks) were assessed. Certainty was graded using GRADE for DTA. Results Thirty-nine studies met criteria (predominantly CT; CNN-based). Pooled sensitivity/specificity were as follows: TB 0.895/0.935, COVID-19 0.872/0.914, nodules/cancer 0.886/0.869, COPD/ILA 0.856/0.844; heterogeneity was extreme ( I 2 ≈ 99–100%). PROBAST-AI indicated highest concerns in analysis and predictors; CLAIM adherence was uneven (external validation: 33%, prospective evaluation: 12%). AI reduced reporting time by ∼20 to 40% and supported low-dose CT with ∼25 to 40% CTDIvol reductions while maintaining sensitivity. Deeks' plots suggested modest asymmetry. GRADE certainty was moderate (COVID-19, nodules/cancer), low–moderate (COPD), and low (TB). Conclusion AI demonstrates promising diagnostic performance across respiratory CT tasks but faces generalizability, bias, and reporting gaps. Prospective multicenter validation, standardized protocols and dose reporting, calibrated/transparent models with quantitatively validated explainability, and assistive workflow deployment are essential for safe, reliable clinical adoption.