Background: Pleural diseases represent a significant healthcare burden, affecting over 350 000 patients annually in the US alone and requiring accurate diagnostic approaches for optimal management. Traditional imaging techniques have limitations in differentiating various pleural disorders and invasive procedures are usually required for definitive diagnosis.Methods: We conducted a nonsystematic, narrative literature review aimed at describing the latest advances in imaging techniques and artificial intelligence (AI) applications in pleural diseases.Results: Novel ultrasound-based techniques, such as elastography and contrast-enhanced ultrasound, are described for their promising diagnostic accuracy in differentiating malignant from benign pleural lesions. Quantitative imaging techniques utilising pixel-density measurements to noninvasively distinguish exudative from transudative effusions are highlighted. AI algorithms, which have shown remarkable performance in pleural abnormality detection, malignant effusion characterisation and automated pleural fluid volume quantification, are also described. Finally, the role of deep-learning models in early complication detection and automated analysis of follow-up imaging studies is examined.Conclusions: Advanced imaging techniques and AI applications show promise in the management and follow-up of pleural diseases, improving diagnostic accuracy and reducing the need for invasive procedures. However, larger prospective studies are needed for validation. The integration of AI-driven imaging analysis with molecular and genomic data offers potential for personalised therapeutic strategies, although challenges in data privacy, algorithm transparency and clinical validation persist. This comprehensive approach may revolutionise pleural disease management, enhancing patient outcomes through more accurate, noninvasive diagnostic strategies.

Background: Drug-induced interstitial lung disease (DI-ILD) encompasses a heterogeneous spectrum of potentially severe pulmonary toxicities associated with various pharmacological agents. Diagnosis is often delayed due to nonspecific symptoms and imaging findings that can mimic other ILDs or infections, particularly in patients receiving oncological or immunomodulatory therapies. Novel approaches are needed to improve early recognition and management. Methods: We conducted a non-systematic, narrative literature review across major biomedical databases up to June 2025, aimed at describing current and emerging applications of artificial intelligence (AI) and radiomics in the early diagnosis, risk stratification, and personalised management of DI-ILD. Results: Radiomics applied to computed tomography and positron emission tomography/computed tomography enables extraction of high-dimensional quantitative features capturing subclinical alterations undetectable by visual assessment. In DI-ILD (particularly immune checkpoint inhibitor-related pneumonitis), radiomic models show potential diagnostic utility in distinguishing overlapping imaging patterns and predicting fibrotic progression. Integration with clinical, radiological and pharmacogenomic data has improved model performance in several studies. Additionally, AI approaches, including convolutional neural networks and ensemble learning methods, demonstrate promise in enhancing pattern recognition and risk stratification. Conclusions: Radiomics and AI are emerging complementary tools in multidisciplinary management of DI-ILD, offering objective imaging biomarkers and facilitating multimodal data integration to improve diagnostic precision and personalised therapeutic decisions. Nonetheless, reproducibility remains limited by variability in imaging protocols and lack of large-scale prospective multicentre validation. Clinical implementation requires standardised protocols to ensure consistency and reliability. The development of transparent, interpretable models seamlessly integrated into healthcare workflows is essential to fully leverage their real-world potential in proactive patient care.

Idiopathic pulmonary fibrosis (IPF) is associated with a significantly increased risk of thrombotic events and mortality. This review explores the complex bidirectional relationship between pulmonary fibrosis and thrombosis, discussing epidemiological evidence, pathogenetic mechanisms, and therapeutic implications, with a particular focus on the emerging role of extracellular vesicles (EVs) as crucial mediators linking fibrosis and coagulation. Coagulation factors directly promote fibrosis, while fibrosis itself activates thrombotic pathways. Retrospective studies suggest the benefits of anticoagulants in IPF, but prospective trials have faced challenges. Novel anticoagulants, profibrinolytic therapies, and agents targeting protease-activated receptors (PARs) show promise in preclinical studies and early clinical trials. EVs have emerged as key players in the pathogenesis of interstitial lung diseases (ILDs), serving as vehicles for intercellular communication and contributing to both fibrosis and coagulation. EV-based approaches, such as EV modulation, engineered EVs as drug delivery vehicles, and mesenchymal stem cell-derived EVs, represent promising therapeutic strategies. Ongoing research should focus on optimizing risk–benefit profiles, identifying predictive biomarkers, evaluating combination strategies targeting thrombotic, fibrotic, and inflammatory pathways, and advancing the understanding of EVs in ILDs to develop targeted interventions.

Hemorrhagic complications during pleural interventions—such as thoracentesis and chest tube insertion—remain a significant clinical concern, primarily due to inadvertent injury of the intercostal artery (ICA). The highly variable ICA anatomy is frequently not visualized on conventional imaging, limiting the reliability of landmark-based techniques. Color Doppler thoracic ultrasound (CDUS) has emerged as a non-invasive, real-time modality capable of identifying ICAs and their anatomical variants prior to pleural access. This narrative review synthesizes current evidence on CDUS-guided ICA screening, focusing on its technical principles, diagnostic performance, and clinical applicability. While feasibility and utility are supported by multiple observational studies, robust evidence demonstrating a reduction in bleeding complications is still lacking. Barriers to widespread implementation include heterogeneous scanning protocols, operator dependency, and the absence of standardized training. We discuss the anatomical rationale for pre-procedural vascular mapping and highlight emerging protocols aimed at standardizing ICA visualization. Although not yet incorporated into major clinical guidelines, CDUS represents a promising tool to enhance procedural safety. Emerging AI applications may further improve vessel detection by reducing operator dependency and enhancing reproducibility. High-quality prospective studies are essential to validate potential clinical benefits, optimize implementation strategies, and support integration into routine pleural practice.

The large airways, extending from the trachea to the main bronchi, respond dynamically to exercise‐induced ventilatory demands. Large airway collapse (LAC) represents a spectrum of conditions characterized by excessive reduction in tracheal and/or main bronchial lumen during expiration. Utilizing the most common diagnostic criteria, defined as ≥50% reduction in airway cross‐sectional area during expiration, LAC is a common finding in around one in three patients with underlying lung disease. However, it is also apparent that healthy, asymptomatic people meet this diagnostic criteria. Despite being recognized as a cause of exertional symptoms, the relationship between LAC and exercise‐related symptoms or limitation is currently poorly understood. Traditional clinical approaches use forced expiratory measurements performed at rest during bronchoscopy or imaging studies to assess the condition. But novel tests, visualizing the large airways during exercise, may provide more physiologically relevant insight and are an important next step towards the development of targeted interventions for this clinical entity. This review aims to examine large airway behavior during different ventilatory challenges, with particular focus on comparing exercise hyperpnea with forced expiratory maneuvers.

This study demonstrates that cardiopulmonary exercise testing with concurrent continuous bronchoscopy provides measurement of pulmonary gas exchange, ventilation, and visualization of large airway movement during vigorous exercise. Although oxygen uptake and carbon dioxide output were not altered during exercise with bronchoscopy, peak exercise ventilation and markers of effort were lower in these trials.