Background: Artificial intelligence (AI) and deep learning are increasingly applied in cardiovascular imaging. However, the “black box” nature of these models raises challenges for clinical trust and integration. Explainable Artificial Intelligence (XAI) seeks to address these concerns by providing insights into model decision-making. This systematic review synthesizes current research on the use of XAI methods in radiological cardiovascular imaging. Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science to identify peer-reviewed original research articles published between January 2015 and March 2025. Studies were included if they applied XAI techniques—such as Gradient-Weighted Class Activation Mapping (Grad-CAM), Shapley Additive Explanations (SHAPs), Local Interpretable Model-Agnostic Explanations (LIMEs), or saliency maps—to cardiovascular imaging modalities, including cardiac computed tomography (CT), magnetic resonance imaging (MRI), echocardiography and other ultrasound examinations, and chest X-ray (CXR). Studies focusing on nuclear medicine, structured/tabular data without imaging, or lacking concrete explainability features were excluded. Screening and data extraction followed PRISMA guidelines. Results: A total of 28 studies met the inclusion criteria. Ultrasound examinations (n = 9) and CT (n = 9) were the most common imaging modalities, followed by MRI (n = 6) and chest X-rays (n = 4). Clinical applications included disease classification (e.g., coronary artery disease and valvular heart disease) and the detection of myocardial or congenital abnormalities. Grad-CAM was the most frequently employed XAI method, followed by SHAP. Most studies used saliency-based techniques to generate visual explanations of model predictions. Conclusions: XAI holds considerable promise for improving the transparency and clinical acceptance of deep learning models in cardiovascular imaging. However, the evaluation of XAI methods remains largely qualitative, and standardization is lacking. Future research should focus on the robust, quantitative assessment of explainability, prospective clinical validation, and the development of more advanced XAI techniques beyond saliency-based methods. Strengthening the interpretability of AI models will be crucial to ensuring their safe, ethical, and effective integration into cardiovascular care.

Background/Objectives: This study aimed to investigate pneumothorax risk, focusing on the gravitational effect of pleural pressure caused by specific patient positioning. Methods: We retrospectively analyzed 144 percutaneous CT-guided lung biopsies performed between January 2019 and December 2023. Patients were grouped into those with or without pneumothorax. Variations in patient positioning (prone, supine, lateral, lesion in decubitus biopsy-side-down [LD BSD] and the dependent area [L DA M], and access route beginning in the dependent area [AR LD M]) were compared using the chi-square, Fisher’s exact, and Mann–Whitney U tests. Performance metrics were evaluated. Univariate and binomial logistic regression models assessed the influence of these factors and other patient-related and interventional parameters on pneumothorax occurrence. Results: Three positional variants (AR DA M, L DA M, and L LD BSD; p < 0.001), general emphysema (p = 0.009), emphysema in the access route (p = 0.025), greater needle size (18G vs. 20G; p < 0.001), and the use of a side-cut instead of a full-core system (p = 0.002) were significantly linked to lower peri-interventional pneumothorax incidence. Even after adjusting for various factors, AR DA M and general emphysema remained independently associated with a reduced pneumothorax risk (OR 0.168, p < 0.001; OR 2.72, p = 0.034). Assessing the dependent zones showed superior performance regardless of the patient’s position, with the best performance demonstrated for AR DA M (AUC 0.705; sensitivity 60%, specificity 81.8%). Conclusions: Focusing on the dependent zones of each lung and adjusting the access route accordingly can significantly reduce the risk of pneumothorax compared to conventional positioning techniques.

Background/Objectives: To characterize the zero-shot diagnostic behavior of three commercial multimodal large language models (MLLMs) on cardiopulmonary chest radiograph findings and to assess sex-stratified performance differences. Methods: GPT-5.4, Claude Opus 4.5, and Gemini 2.5 Pro were evaluated in 4500 pathology-specific radiograph evaluations based on frontal chest radiographs from the publicly available CheXpert dataset. Three balanced cohorts of 1500 images each were constructed for cardiomegaly, pulmonary edema, and pleural effusion (375 per sex-by-label subgroup). All models received identical zero-shot prompts requesting binary classification. The primary outcome was area under the receiver operating characteristic curve (AUC-ROC) with 95% bootstrap confidence intervals. Secondary outcomes were sensitivity and specificity. Results: A total of 4500 pathology-specific radiograph evaluations were performed across the three cohorts (2250 male and 2250 female cohort entries; mean age 58.4 ± 18.0 years). GPT-5.4 achieved the highest discrimination (AUC-ROC 0.836–0.883) but showed very low sensitivity (0.043–0.424) with near-perfect specificity (0.977–0.997). Claude Opus 4.5 showed moderate discrimination (AUC-ROC 0.698–0.761) with balanced sensitivity (0.396–0.876) and specificity (0.461–0.863). Gemini 2.5 Pro showed moderate discrimination (AUC-ROC 0.745–0.770) but favored sensitivity (0.673–0.973) at the expense of specificity (0.241–0.804). Sex-stratified analyses showed consistently higher AUC point estimates in male patients for cardiomegaly and pulmonary edema, but smaller and less directional differences for pleural effusion. Conclusions: Commercial MLLMs differ considerably in operating profiles, ranging from ultraconservative to aggressive detection, so that strong aggregate discrimination can mask sensitivity too low for reliable detection. None of the evaluated models are currently suitable for autonomous chest radiograph interpretation. Sex-stratified differences were modest but non-uniform, supporting subgroup-aware reporting rather than reliance on pooled metrics alone.

Objectives: To evaluate the prevalence, classification, and clinical relevance of incidental extracardiovascular findings in pre-transcatheter aortic valve implantation (TAVI) CT imaging. Methods: We conducted a retrospective single-center study of 225 patients undergoing pre-TAVI contrast-enhanced, ECG-gated CT scans between 2021 and 2023. Extracardiovascular findings were recorded and categorized into three groups based on presumed clinical relevance: Group A (findings with no need for follow-up), Group B (findings requiring follow-up), and Group C (findings requiring immediate intervention or treatment). Statistical analysis included a descriptive assessment of the overall prevalence of incidental findings and evaluation of age- and sex-related trends using chi-square tests with Bonferroni-adjusted pairwise comparisons. Results: The study cohort included 225 patients (53.3% male; mean age 79.9 ± 6.2 years, range 58–93). Extracardiovascular incidental findings were detected in 205 patients (91.1%). Among all 478 recorded findings, 82.6% were Group A, 14.4% Group B, and 2.9% Group C. On a per-patient level, 87.1% had at least one Group A finding, 24.9% had at least one Group B, and 6.2% had at least one Group C finding. Older age was associated with more incidental findings, with a significant difference observed between the 70–79 and 80–89 age groups (p = 0.002). No significant sex-related differences were found (p = 0.226). Findings were most frequently located in the abdomen (46.2%) and thorax (37.2%). Among all clinically relevant findings, the thorax was the most commonly affected region: 43.5% of Group B and 78.6% of Group C findings were located in the thorax, followed by the abdomen (33.3% of Group B and 7.1% of Group C findings). Conclusions: Extracardiovascular incidental findings are highly prevalent in pre-TAVI CT imaging and range from benign, age-related changes to potentially serious conditions such as malignancies or infections. Their presence reflects the comorbidity burden of the typical TAVI population and underscores the importance of recognizing non-vascular incidental findings in this clinical setting.