Pulmonary embolism (PE) remains a critical condition with significant mortality and morbidity, necessitating timely detection and intervention to improve patient outcomes. This review examines the evolving role of artificial intelligence (AI) in PE management. Two primary AI-driven models that are currently being explored are deep convolutional neural networks (DCNNs) for enhanced image-based detection and natural language processing (NLP) for improved risk stratification using electronic health records. A major advancement in this field was the FDA approval of the Aidoc© AI model, which has demonstrated high specificity and negative predictive value in PE diagnosis from imaging scans. Additionally, AI is being explored for optimizing anticoagulation strategies and predicting PE recurrence risk. While further large-scale studies are needed to fully establish AI’s role in clinical practice, its integration holds significant potential to enhance diagnostic accuracy and overall patient management.

Over the last decade, immunotherapy has transformed oncology, with an ever-expanding list of indications. As with any therapeutic class, these agents are accompanied by adverse events that are increasingly recognized and pose new challenges for patient care expanding the scope of cardio-oncology. Using the PubMed database, this narrative review summarizes the cardiovascular adverse events associated with the most commonly used immunotherapies, immune checkpoint inhibitors (ICIs), with a focus on the mechanisms, epidemiology, diagnosis, treatment, and monitoring of ICI-associated myocarditis. We also review accelerated atherosclerotic disease, heart failure, cardiomyopathy (including stress-induced cardiomyopathy), arrhythmias, and pericardial disease related to ICI therapy. Finally, we outline CAR T-cell therapy associated cardiotoxicity, including cytokine release syndrome.