Lung cancer ranks first in morbidity and mortality among malignant tumors in China. Low-dose computed tomography (LDCT), as the primary screening method, faces increasingly prominent challenges of false positives and overdiagnosis associated with its high sensitivity. In recent years, the integration of artificial intelligence (AI) and three-dimensional reconstruction technology has provided a novel approach to addressing these challenges. This review highlights how AI and three-dimensional reconstruction contribute to improving image quality, enabling precise identification of pulmonary nodules and intelligent risk stratification, optimizing dynamic follow-up strategies, and assisting in preoperative planning, thereby driving the transformation of the LDCT screening paradigm. Meanwhile, current technical limitations and future directions are also discussed.
Against the backdrop of high-quality development in public hospitals, enhancing emergency and critical care capabilities has become a core task in medical system construction. Based on the resilience theory’s “4R model” (robustness, redundancy, resourcefulness, and rapidity) and three key principles of synergy theory (amplification, dominance, and order parameters), Shanghai Chest Hospital has developed and implemented an integrated emergency-critical care operational model. This article systematically introduces the model’s construction background, theoretical framework, and core implementation measures, primarily including management synergy, resource integration, and smart empowerment. Practical results demonstrated that the model effectively improved treatment quality, optimized resource efficiency, and strengthened disciplinary capabilities. By sharing this replicable practical experience, it provides theoretical references and practical paradigms for the systematic optimization and resilience enhancement of emergency and critical care systems in public hospitals.