Background: Interstitial lung disease (ILD) encompasses a heterogeneous group of disorders characterized by lung inflammation and fibrosis. Current diagnostic and management approaches for ILD are limited by suboptimal accuracy and efficacy. This study aimed to evaluate innovative approaches in ILD diagnosis and management. Methods: In this prospective, multi-center study, 500 patients with suspected or confirmed ILD were randomized to receive either standard care or personalized treatment guided by artificial intelligence (AI)-assisted high-resolution computed tomography (HRCT) analysis, a novel biomarker panel, and an integrated treatment algorithm. Diagnostic accuracy, clinical outcomes, and cost-effectiveness were assessed over 24 months. Results: AI-assisted HRCT analysis demonstrated superior diagnostic accuracy compared to conventional radiologist interpretation (AUC 0.945 vs 0.891, p<0.001). The novel biomarker panel showed high sensitivity (83.9-88.3%) and specificity (89.4-92.1%) for ILD subtype differentiation. A new minimally invasive lung sampling technique exhibited comparable diagnostic yield to surgical biopsy (87.5% vs 92.0%, p=0.24) with significantly fewer complications (3.5% vs 12.5%, p<0.001). Patients receiving personalized treatment showed significantly better preservation of lung function (FVC decline: -50 mL vs -150 mL, p<0.001), exercise capacity, and quality of life at 12 months compared to standard care. The personalized approach resulted in a 15% reduction in healthcare utilization costs over 24 months (p<0.001). Conclusion: Innovative approaches incorporating AI-assisted imaging analysis, novel biomarkers, and personalized treatment algorithms significantly improved diagnostic accuracy, clinical outcomes, and cost-effectiveness in ILD management. These findings support the integration of these approaches into clinical practice and warrant further investigation in larger, long-term studies.