| Description |
Large Language Models (LLMs) are a type of machine learning, a subset of artificial intelligence (AI), capable of learning language and generating human-like text in response to input they receive. These models are trained on vast amounts of data, allowing them to recognize patterns, context, and semantics in language. Over the last decade, LLMs have increased in quantity and quality, showing strong performance in language generation, information retrieval, and, more recently, complex reasoning (Bathaee, 2018). Many fields have begun integrating LLMs, streamlining or completely automating many processes that require language comprehension. In healthcare and especially emergency medicine, where accuracy and efficiency are necessary for complex situations, LLMs can support healthcare professionals in several ways. As the capabilities of LLMs rapidly expand, their utility and impact are only beginning to be realized. A wide variety of AI technologies are already being integrated or tested in clinical applications. This honors thesis explores the emerging applications of LLMs in Emergency Medical Services (EMS), in three main areas: triage, documentation, and quality assurance. I will devote the most attention to this latter application. Building on a recent publication that I coauthored exploring the potential of LLMs to perform quality assurance, I will propose updated methods that address limitations based on recent innovations. Additionally, I will address the practical challenges of implementing an AIassisted quality assurance system in a real EMS agency. I believe that proactive consideration of the ethical and effective use of AI-generated clinical assistance in EMS is essential to safely maximize their potential impact. |
