The American journal of emergency medicine
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Artificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART score, to guide clinical decision-making. This study aims to evaluate the proficiency of GPT-3.5 and GPT-4 against experienced ED physicians in calculating five commonly used medical scores. ⋯ While AI models demonstrated some level of concordance with human expertise, they fell short in emulating the complex clinical judgments that physicians make. The study suggests that current AI models may serve as supplementary tools but are not ready to replace human expertise in high-stakes settings like the ED. Further research is needed to explore the capabilities and limitations of AI in emergency medicine.
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High-risk pulmonary embolism (PE) is a complex, life-threatening condition, and emergency clinicians must be ready to resuscitate and rapidly pursue primary reperfusion therapy. The first-line reperfusion therapy for patients with high-risk PE is systemic thrombolytics (ST). Despite consensus guidelines, only a fraction of eligible patients receive ST for high-risk PE. ⋯ Emergency clinicians must possess an understanding of high-risk PE including the clinical assessment, pathophysiology, management of hemodynamic instability and respiratory failure, and primary reperfusion therapies.