Artificial Intelligence Versus Radiologists in Predicting Lung Cancer Treatment Response: A Systematic Review and Meta-Analysis

Nehemias Guevara Rodriguez^1,2*Noemy Coreas^3,4Binay Kumar Panjiyar^5,6Ranju Kunwor^1,7

• ¹Saint Louis University, St Louis, United States
• ²Deparment of Medicine, Division of Hematology Oncology and Bone Marrow Transplant, St. Louis, United States
• ³Universidad de El Salvador, San Salvador, El Salvador
• ⁴Departmetn of OBGYN Oncology, Social Security System of El Salvador, San Salvador, El Salvador
• ⁵Johns Hopkins University, Baltimore, United States
• ⁶Johns Hopkins University Department of Medicine, Baltimore, United States
• ⁷Department of Medicine, Division of hematology Oncology and Bone Marrow Transplant, St. Louis University, Saint Louis, United States

Background: Artificial intelligence (AI) has emerged as a promising adjunct to radiologist interpretation in oncology imaging. This systematic review and meta-analysis compares the diagnostic performance of AI systems versus radiologists in predicting lung cancer treatment response, focusing solely on treatment response rather than diagnosis. Methods: We systematically searched PubMed, Embase, Scopus, Web of Science, and the Cochrane Library from inception to March 31, 2025; Google Scholar and CINAHL were used for citation chasing/grey literature. The review protocol was prospectively registered in PROSPERO (CRD420251048243). Studies directly comparing AI-based imaging analysis with radiologist interpretation for predicting treatment response in lung cancer were included. Two reviewers extracted data independently (Cohen's κ = 0.87). We pooled sensitivity, specificity, accuracy, and risk differences using DerSimonian–Laird random-effects models. Heterogeneity (I²), threshold effects (Spearman correlation), and publication bias (funnel plots, Egger's test) were assessed. Subgroups were prespecified by imaging modality and therapy class. Results: Eleven retrospective studies (n = 6,615) were included. Pooled sensitivity for AI was 0.9 (95% CI: 0.8–0.9; I² = 58%), specificity 0.8 (95% CI: 0.8–0.9; I² = 52%), and accuracy 0.9 (95% CI: 0.8–0.9; pooled OR = 1.4, 95% CI: 1.2–1.7). Risk difference favored AI by 0.06 for sensitivity and 0.04 for specificity. AI's advantage was most apparent in CT and PET/CT, with smaller/non-significant gains in MRI. Egger's test suggested no significant publication bias (p = 0.21). Conclusion: AI demonstrates modest but statistically significant superiority over radiologists in predicting lung cancer treatment response, particularly in CT and PET/CT imaging. However, generalizability is limited by retrospective study dominance, incomplete demographic reporting, lack of regulatory clearance, and minimal cost-effectiveness evaluation. Prospective, multicenter trials incorporating explainable AI (e.g., SHAP, Grad-CAM), equity assessments, and formal economic analyses are needed.