Bowen Wu, Sha Lu, Haipeng Liu
Bronchiectasis is a complex, chronic airway syndrome driven by a vicious cycle of irreversible bronchial dilatation, impaired mucociliary clearance, recurrent infection, and tissue-destructive inflammation. Reflecting its profound clinical heterogeneity, patients with identical structural damage on high-resolution computed tomography (HRCT) often exhibit divergent profiles in airway microbiology, inflammatory endotypes, exacerbation frequencies, and therapeutic responses, indicating that static anatomical classification fails to capture disease complexity. Sole reliance on visual CT inspection, standard sputum cultures, and subjective symptom tracking misses the driving mechanisms of individual disease progression. Emerging modalities-artificial intelligence (AI)-driven quantitative imaging, molecular microbiology, high-throughput biomarker profiling, and digital remote monitoring-aim to address these gaps. Our analysis shows that while these tools cannot substitute for bedside clinical acumen, they clarify obscure phenotypes, expose actionable treatable traits, and enable earlier, preemptive strategies. This review evaluates these contemporary diagnostic frameworks in non-cystic fibrosis bronchiectasis, dissecting their clinical utility, evidentiary maturity, and the economic and logistical barriers to routine adoption. Given that current evidence remains fragmented, advancing the field demands standardized imaging protocols, transparent algorithmic pipelines, clinically actionable metagenomic reporting, and robust validation in underrepresented Asian and Chinese cohorts. The real challenge lies not in generating more data, but in integrating these heterogeneous, high-dimensional datasets into pragmatic, point-of-care decision pathways that improve patient outcomes without widening disparities in global healthcare delivery.