Imagine AI scanning medical images to swiftly detect life-threatening blood clots, like venous thromboembolism (VTE). This isn't science fiction—it's the focus of groundbreaking research exploring how language models can analyze radiology reports to identify VTE, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). A recent study pitted a novel architecture called "Mamba" against large language models (LLMs) and traditional methods like BERT. Mamba, inspired by state space models (SSMs), boasts incredible efficiency in processing long text sequences. The researchers found that Mamba achieved remarkable accuracy, scoring 97% on DVT and a near-perfect 98% on PE datasets. Even more impressive, Mamba outperformed a hybrid system that combined deep learning with complex, hand-crafted rules. While a smaller LLM called Phi-3 Mini also showed promising results, exceeding the performance of baseline BERT models, its larger size made it computationally expensive. Mamba's efficiency and accuracy offer a compelling alternative, particularly in clinical settings where resources might be limited. This research paves the way for streamlined, automated VTE detection, potentially saving lives through faster diagnosis. Future work aims to optimize Mamba for deployment on various clinical devices, from resource-constrained edge devices to powerful cloud-based systems. This could revolutionize how we detect VTE, enabling quicker interventions and better patient outcomes.