The use of immune checkpoint inhibitors (ICIs) has increased exponentially in recent years, leading to a significant impact on cancer patient survival. However, their administration can trigger immune-mediated adverse effects, notably pulmonary toxicity, which is a potentially serious complication. ICI-induced pneumonitis has a variable incidence ranging from 5 to 19% and usually appears in the first few months of treatment. The diagnosis requires a high index of suspicion, especially in patients with risk factors (elderly male smokers with squamous cell lung cancer, previous respiratory or autoimmune disease, and receiving combination treatment with other ICIs or chemo-radiotherapy). Chest computed tomography (CT) is a key test, allowing the identification of different radiological patterns. This study can be completed with bronchoscopy with bronchoalveolar lavage (BAL) to rule out infection or tumour progression. In general terms, treatment is based on discontinuing the causative drug, with or without the initiation of systemic corticosteroids, escalating to immunosuppressants depending on the severity and/or refractoriness of the condition. This paper provides an updated narrative review of ICI pulmonary toxicity, addressing its pathophysiology, different types of lung damage, diagnostic and therapeutic algorithms, and the emerging role of biomarkers such as KL-6 or IL-6. This article emphasises the need for a multidisciplinary approach and further prospective studies to optimise the management and prognosis of this immune-mediated complication.

Background and Objectives: Alpha-1 antitrypsin deficiency (AATD) is a rare genetic condition associated with chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD) and emphysema, and with liver involvement through a distinct toxic gain-of-function mechanism. Despite its clinical relevance, AATD remains underdiagnosed and exhibits marked phenotypic heterogeneity. Artificial intelligence (AI) has shown growing potential in respiratory medicine, yet its application to AATD is still limited. This systematic review synthesizes the clinical evidence on AI in AATD, primarily in the respiratory domain and, where available, in hepatic outcomes. Materials and Methods: We conducted a PRISMA-guided search (PubMed, Web of Science, IEEE Xplore) for original, peer-reviewed articles (January 2014–September 2025) applying AI to detection, classification, stratification, or prediction tasks in AATD. Results: Six studies met eligibility criteria. Supervised models (e.g., XGBoost, penalized regression, Transformer-based architectures) and one unsupervised approach were identified. Applications included screening in COPD populations, prediction of emphysema progression from CT, proteomic modeling of lung function, identification of clinical subgroups, and prediction of clinical outcomes in AATD-associated liver disease. External validation and genotype diversity remained limited across studies. Conclusions: Although AI shows promise in improving detection, prognosis, and patient stratification in AATD across both respiratory and hepatic manifestations, the current evidence remains limited. Broader, multicenter validation in genotype-diverse cohorts is required to confirm its clinical utility and support the implementation of precision medicine in AATD.

Objective: To identify distinct clinical groups within the Pi*ZZ and Pi*SZ genotypes using unsupervised Machine Learning (ML) methods applied to a large multicenter national cohort, with the aim of describing disease heterogeneity and exploring potential patterns for future stratification. Methods: A cross-sectional multicentre study was conducted using data from the Spanish population enrolled in the EARCO international registry. Patients with severe deficiency (serum AAT < 11 µM or genotypes Pi*ZZ or Pi*SZ) included between February 2020 and December 2024 were analysed. Clinical, functional, and demographic variables were examined using the k-prototypes clustering algorithm, which integrates continuous and categorical data. Differences between clusters were evaluated with Mann–Whitney U and Chi-square tests ( p < 0.05). Results: A total of 697 patients were analyzed (253 Pi*ZZ, 444 Pi*SZ). Four distinct groups were identified within each genotype. In Pi*ZZ, two groups showed predominant parenchymal lung disease with emphysema and variable obstruction severity, one consisted mainly of middle-aged non-smoking women with bronchiectasis, and another included young, asymptomatic individuals with normal lung function. Among Pi*SZ, the phenotypic spectrum ranged from asymptomatic subjects identified by family screening to patients with advanced COPD, emphysema, and high comorbidity burden. Cardiovascular and metabolic comorbidities contributed to the characterization of disease expression, particularly in Pi*SZ. Conclusions: Unsupervised ML allowed the identification of clinically coherent descriptive profiles within severe alpha-1 antitrypsin deficiency, highlighting the wide heterogeneity of its respiratory manifestations. This stratification may contribute to a more individualized understanding of disease expression and provide a framework for future longitudinal validation.