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[This corrects the article DOI: 10.7759/cureus.34400.].

Left ventricular (LV) thrombosis usually occurs as a complication of acute anterior myocardial infarction (MI) and dilated cardiomyopathy. It also occurs in patients with a hypercoagulable state. However, in the setting of normal systolic function, LV thrombi are extremely rare. We present a case of a healthy woman who had LV thrombus despite normal LV systolic function that presented as an acute aortoiliac embolism.

Radiotherapy is the cornerstone of brain metastasis management. With the advancement of therapies, patients are living longer, exposing them to the long-term effects of radiotherapy. Using concurrent or sequential chemotherapy, targeted agents, and immune checkpoint inhibitors may increase the incidence and severity of radiation-induced toxicity. Recurrent metastasis and radiation necrosis (RN) appear indistinguishable on neuroimaging, making it a diagnostic dilemma for clinicians. Here, we present a case of RN in a 65-year-old male patient who previously had brain metastasis (BM) from primary lung cancer, misdiagnosed initially as recurrent BM.

Diagnosis and treatment of subsegmental pulmonary embolism (SSPE) are challenging. Contrary to segmental pulmonary emboli (PE), little is known about the accuracy of the diagnosis of SSPE. We aimed to assess the accuracy of SSPE in our retrospective cohort study. Patients with isolated SSPE were included. Concurrent segmental PE or deep venous thrombosis (DVT) were considered an exclusion criteria. Another radiologist reviewed each CTA. A total of 43 patients with SSPE were identified. Forty patients (93%) received therapeutic anticoagulation. The average duration of anticoagulation therapy was four months. Another radiologist's review of the CTA on admission revealed a discordant diagnosis: 13 out of 43 (30% | cases were reported negative for SSPE. One case was reported for segmental PE. As a conclusion, the accuracy of the diagnosis of SSPE is low as the discordance rate by different radiologists is high. More studies are needed to establish the diagnosis and guide treatment of SSPE.

Methaemoglobinaemia is defined as elevated methaemoglobin in the blood which is characterised by conversion of some of the reduced ferrous iron elements [Fe2+] to the oxidised ferric [Fe3+] form which does not have capacity to bind and transport oxygen resulting in functional anaemia. Causes can be genetic mutations or acquired by medications such as dapsone, nitrates or benzocaine. Benzocaine is currently being used as a topical anaesthetic agent before certain procedures. We report a case of benzocaine spray–induced methaemoglobinaemia in a patient who underwent oesophagogastroduodenoscopy for evaluation of upper gastrointestinal bleeding.

We report a case of emphysematous endometritis in a 65-year-old patient who has stage III, high-grade, poorly differentiated endometrial cancer; she was on chemotherapy. The patient developed pyogenic emphysematous endometritis complicated by hypovolemic shock and sepsis. She was admitted to the intensive care unit for treatment with vasopressors and antibiotics. The shock was successfully managed and her hospital course was otherwise unremarkable.

Introduction Cardiac arrhythmias remain a leading cause of cardiovascular morbidity and mortality worldwide, and conventional diagnostic tools such as electrocardiography and Holter monitoring may fail to detect transient or asymptomatic events. Recent advances in artificial intelligence (AI) and machine learning have enhanced arrhythmia detection, risk stratification, and treatment planning; however, most existing models rely primarily on statistical associations rather than underlying physiological mechanisms. Methods This narrative review was conducted through a structured but non‐systematic literature search of PubMed, Scopus, and Google Scholar, covering studies published between 2000 and 2025. Search terms included combinations of “cardiac arrhythmia,” “atrial fibrillation,” “causal inference,” “structural causal models,” “digital twins,” “mechanistic modeling,” “cardiac electrophysiology modeling,” and “artificial intelligence.” Peer‐reviewed articles were included if they demonstrated methodological depth and addressed causal inference or mechanistic modeling approaches in cardiovascular research, particularly in arrhythmia detection, risk prediction, treatment optimization, or clinical validation frameworks. Studies were prioritized based on methodological rigor, translational relevance, and recency. Editorials lacking methodological detail, non‐English publications, and studies relying solely on predictive models without incorporating causal or mechanistic components were excluded. Results Causal artificial intelligence (Causal AI), offers a more mechanistically grounded framework for understanding arrhythmogenesis and therapeutic outcomes. Emerging evidence suggests that integrating clinical data with structural causal models, mechanistic modeling, and patient‐specific digital twins can bridge the gap between predictive performance and physiological interpretability. These approaches show promise in predicting ablation success, guiding therapy, and improving individualized care. Conclusion Despite this potential, clinical implementation remains limited due to data heterogeneity, validation challenges, and regulatory constraints. Causal artificial intelligence (AI) in Cardiac Arrhythmia: From Pattern Recognition to Mechanistic Insight. Multimodal cardiac data, including electrocardiograms (ECG), wearable device recordings, cardiac imaging, genetic information, and electronic health records, are integrated to support AI‐based analysis. Traditional AI approaches primarily rely on pattern recognition and correlation‐based predictions. In contrast, causal AI models incorporate causal inference and structural causal modeling to identify cause effect relationships and generate mechanistic insights. These approaches can improve early arrhythmia detection, risk prediction, treatment planning, and patient‐specific cardiac modeling, ultimately contributing to more precise and personalized cardiac care. Key challenges include data heterogeneity, the need for rigorous clinical validation, and regulatory considerations.

This research explores the perception of patient protection culture among emergency nurses at King Saud Medical City. The study examines key dimensions of safety culture, including teamwork, communication, error reporting, and leadership support. Utilizing a descriptive and analytical approach, data were collected via the Hospital Survey on Patient Safety Culture (HSOPSC), involving 210 nurses across pediatric, maternity, and general emergency departments. Findings indicate that 46.7% of nurses rated the safety culture as “acceptable,” with 40% assessing it as “very good.” Critical strengths include teamwork within units (74%) and leadership acknowledgment of safety adherence (76.67%). However, challenges persist, such as inadequate staffing levels (65.33%) and fear of punitive repercussions for errors (76%), which hinder open reporting. Additionally, communication breakdowns between hospital units (76.67%) were identified as a major issue. Demographic analysis revealed that younger nurses and expatriates face unique challenges, including lower perceptions of communication effectiveness. The research underscores the importance of fostering a non-punitive culture, improving interdepartmental communication, and implementing structured safety protocols. The study concludes that while a foundational understanding of safety culture exists, targeted interventions are needed to enhance communication, leadership engagement, and error reporting. These findings offer actionable insights for healthcare administrators to strengthen patient safety and improve the overall quality of care in emergency settings