Despite rapid proliferation of AI applications in prostate MRI showing impressive technical performance, clinical adoption remains limited. We conducted a comprehensive narrative review of literature from January 2018 to December 2024, examining AI applications in prostate MRI with emphasis on real-world performance and implementation challenges. Among 200+ studies reviewed, AI systems achieve 87% sensitivity and 72% specificity for cancer detection in research settings. However, external validation reveals average performance drops of 12%, with some implementations showing degradation up to 31%. Only 31% of studies follow reporting guidelines, 11% share code, and 4% provide model weights. Seven real-world implementation studies demonstrate integration times of 3–14 months, with one major center terminating deployment due to unacceptable false positive rates. The translation gap between artificial and clinical intelligence remains substantial. Success requires shifting focus from accuracy metrics to patient outcomes, establishing transparent reporting standards, developing realistic economic models, and creating appropriate regulatory frameworks. The field must combine methodological rigor, clinical relevance, and implementation science to realize AI’s transformative potential in prostate cancer care.

The use of transfusions, whether involving whole blood or specific blood components, is essential for managing various clinical conditions. Many cases are acute, often requiring post-transfusion imaging evaluation. While there is no absolute contraindication for chest imaging following blood transfusion, it should be approached cautiously. We conducted a comprehensive search across multiple databases and registries. Research studies were limited to full-text original articles, reviews, and case reports published in English, involved human subjects, and focused on the interplay between blood transfusions and contrast-enhanced imaging. Scientific analyses were excluded if they did not focus on transfusion practices in the context of imaging or failed to address issues such as hemoglobin thresholds, transfusion reactions, or the clinical implications of contrast agents. Our research fills this gap by emphasizing the need for a cautious, multidisciplinary approach to post-transfusion computed tomography (CT) scans, especially in the presence of contrast agents. This study calls for increased awareness of the heightened risk of complications, such as autoimmune hemolysis, when both procedures are performed together. New insights from our research recommend individualized assessments and close patient monitoring when combining these interventions. Nevertheless, patients need to be hemodynamically and clinically stable before undergoing CT. Discussions. Symptoms that develop within the first 24 h post-transfusion are classified as secondary post-transfusion reactions unless proven otherwise. The prevalence of side effects from same-day CT scans and blood transfusions is challenging to quantify, as few studies focus on this combination. Transfusions and contrast-enhanced CT scans share overlapping adverse reactions and carry significant risks. Acute hemolytic red blood cell transfusion reactions are among the most frequent side effects, with a prevalence of 1:12,000–38,000. Conclusion. Our study contributes new insights to the literature by filling the gap concerning the interplay between transfusions and contrast media, paving the way for more informed clinical protocols to enhance patient safety.

Urinary tract infections (UTIs) sit at a leading edge of the antimicrobial-resistance (AMR) crisis, and Romania ranks among the most affected European countries, yet large single-center series of urinary isolates with complete antibiograms remain scarce. We describe the full resistance landscape of urinary pathogens at a Romanian tertiary hospital, with particular attention to multidrug-resistant Klebsiella and to the often-overlooked urease-positive organisms. We analyzed all positive urine cultures processed over 13 consecutive months (February 2025–February 2026). The first isolate per patient was the primary unit of analysis (n = 2331); the full isolate-level dataset (n = 3348) was analyzed secondarily. Resistance phenotypes (putative ESBL, carbapenem resistance, MDR, VRE) were derived from per-agent susceptibility data following Magiorakos et al., and resistance rates were calculated only on tested isolates. Associations were tested with chi-square/Fisher tests and Benjamini–Hochberg correction. A total of 3348 isolates from 2331 unique patients were analyzed (median age 68 years; 55% male). Escherichia coli predominated (40.5%), followed by Enterococcus spp. (22.2%) and Klebsiella spp. (18.9%). Among the first isolates, 15.3% were MDR; 16.7% of E. coli, Klebsiella and Proteus showed a putative ESBL phenotype; and 3.9% of Enterobacterales were carbapenem-resistant. Resistance was concentrated in Klebsiella (36.2% MDR, 22.8% putative ESBL, 14.7% carbapenem-resistant; all higher than E. coli, p < 0.001) and in Pseudomonas spp. (48.1% carbapenem-resistant). E. coli retained excellent activity to nitrofurantoin (1.2%), fosfomycin (1.2%; tested in a subset) and ertapenem (0.1%) despite high trimethoprim–sulfamethoxazole (31.5%) and fluoroquinolone (24.3%) resistance. Urease-positive organisms formed a distinct subgroup with high trimethoprim–sulfamethoxazole resistance but preserved carbapenem activity. The urinary resistance burden was substantial but uneven, concentrated in Klebsiella and Pseudomonas. These findings support a stratified, locally guided empirical strategy and combined stewardship–infection control efforts.

Background/Objectives: Autism Spectrum Disorder (ASD) is associated with structural brain alterations, particularly involving white matter and connectivity. Artificial intelligence (AI) enhances the detection of subtle neuroanatomical changes. This study aimed to characterize structural abnormalities and volumetric patterns in children with ASD using AI-assisted MRI. Methods: This retrospective study included 90 children diagnosed with ASD. Brain MRI scans were analyzed using the CE-certified AI platform mdbrain. Structural findings were classified into corpus callosum anomalies, white matter signal abnormalities (WMSA), ventriculomegaly, other abnormalities, or no detectable changes. Group differences were assessed using ANOVA and Kruskal–Wallis tests with Tukey post hoc analysis. Logistic regression, principal component analysis (PCA), and linear discriminant analysis (LDA) were applied. Results: WMSA were identified in 23.3% of patients, followed by other anomalies (27.8%), corpus callosum anomalies (8.9%), ventriculomegaly (8.9%), and no abnormalities (31.1%). Total white matter volume was significantly reduced in pathological groups and was the only independent predictor. PCA identified three principal components reflecting shared temporo-parietal covariance, hemispheric asymmetry, and a white matter-related axis. Exploratory LDA demonstrated partial separation among anomaly categories. Conclusions: Children with ASD in this cohort showed heterogeneous but partially structured MRI alterations involving both focal and global volumetric changes. Reduced total white matter volume was the most consistent multivariable association with structural abnormalities. AI-assisted morphometric analysis may support structural phenotyping in ASD. These findings are exploratory and require confirmation in larger, prospectively validated cohorts before biomarker applications can be considered.