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.

Background: Pleural effusion (PE) is a common condition where accurate detection is essential for management. Thoracic ultrasound (TUS) is the first-line modality owing to safety, portability, and high sensitivity, but accuracy is operator-dependent. Artificial intelligence (AI)-based automated analysis has been explored as an adjunct, with early evidence suggesting potential to reduce variability and standardise interpretation. This review evaluates the diagnostic accuracy of AI-assisted TUS for PE detection. Methods: This review was registered with PROSPERO (CRD420251128416) and followed PRISMA guidelines. MEDLINE, Scopus, Google Scholar, IEEE Xplore, Cochrane CENTRAL, and ClinicalTrials.gov were searched through 20 August 2025 for studies assessing AI-based TUS analysis for PE. Eligible studies required recognised reference standards (expert interpretation or chest CT). Risk of bias was assessed with QUADAS-2, and certainty with GRADE. Owing to heterogeneity, structured narrative synthesis was performed instead of meta-analysis. Results: Five studies (7565 patients) published between 2021–2025 were included. All used convolutional neural networks with varied architectures (ResNet, EfficientNet, U-net). Sensitivity ranged 70.6–100%, specificity 67–100%, and AUC 0.77–0.99. Performance was reduced for small, trace, or complex effusions and in critically ill patients. External validation showed attenuation compared with internal testing. All studies had high risk of bias in patient selection and index test conduct, reflecting retrospective designs and inadequate dataset separation. Conclusions: AI-assisted TUS shows promising diagnostic performance for PE detection in curated datasets; however, evidence is inconsistent and limited by key methodological weaknesses. Overall certainty is low-to-moderate, constrained by retrospective designs, limited dataset separation, and scarce external validation. Current evidence is insufficient to support routine clinical use. Robust prospective multicentre studies with rigorous independent validation and evaluation of clinically meaningful outcomes are essential before clinical implementation can be considered.

Leprosy is a neglected disease sporadically reported in high-income countries. Skin lesion and peripheral nerve involvement represent most common manifestations. Mediastinal lymphadenopathy in the absence of superficial lymph nodes involvement is very rare. Atypical or rare clinical presentations of disease may delay diagnosis and therapy and cause potential life-threatening manifestations and disabilities. We describe the case of a 49-year-old Sinhalese man who was admitted to our hospital with a one-month history of peripheral neurological symptoms and skin lesions on lower limbs. CT scan showed the presence of mediastinal lymphadenopathies without lung parenchymal and superficial lymph nodes involvement. Endobronchial ultrasound-guided transbronchial needle aspiration showed the presence of granulomas while skin biopsy revealed dermo-hypodermic granulomas with perineural lymphohistiocytic inflammatory reaction. Fite-Faraco staining demonstrated the presence of acid-fast bacilli in both lymph nodal and skin biopsy and polymerase chain reaction was positive for Mycobacterium leprae. Multibacillary leprosy was then diagnosed.

The pathogenesis of interstitial lung diseases (ILDs) is significantly influenced by genetic factors, yet lack of consensus on the optimal timing for genetic testing and precise patient selection could hinder clinical practice. Position papers currently suggest testing patients presenting with a suspect of Familial Pulmonary Fibrosis (FPF) and with extra-pulmonary syndromic features (i.e., premature graying, cytopenias, liver cirrhosis) for genetic screening. Diagnostics rely on next-generation sequencing (NGS) to identify pathogenic/likely pathogenic variants in telomere-related and surfactant-related genes. A specialized genetic consultation is essential in the correct interpretation of test results, especially when variants of uncertain significance (VUS) are detected. Adoption of other tests, such as polygenic risk scores, could further support precision medicine in ILD care. Future research might address the knowledge gap regarding early test prescription and the role of therapy, including lung transplant stratification and antifibrotic therapy, in FPF.