In the rapidly evolving landscape of medical documentation, transcribing
clinical dialogues accurately is increasingly paramount. This study explores
the potential of Large Language Models (LLMs) to enhance the accuracy of
Automatic Speech Recognition (ASR) systems in medical transcription. Utilizing
the PriMock57 dataset, which encompasses a diverse range of primary care
consultations, we apply advanced LLMs to refine ASR-generated transcripts. Our
research is multifaceted, focusing on improvements in general Word Error Rate
(WER), Medical Concept WER (MC-WER) for the accurate transcription of essential
medical terms, and speaker diarization accuracy. Additionally, we assess the
role of LLM post-processing in improving semantic textual similarity, thereby
preserving the contextual integrity of clinical dialogues. Through a series of
experiments, we compare the efficacy of zero-shot and Chain-of-Thought (CoT)
prompting techniques in enhancing diarization and correction accuracy. Our
findings demonstrate that LLMs, particularly through CoT prompting, not only
improve the diarization accuracy of existing ASR systems but also achieve
state-of-the-art performance in this domain. This improvement extends to more
accurately capturing medical concepts and enhancing the overall semantic
coherence of the transcribed dialogues. These findings illustrate the dual role
of LLMs in augmenting ASR outputs and independently excelling in transcription
tasks, holding significant promise for transforming medical ASR systems and
leading to more accurate and reliable patient records in healthcare settings.

本研究探讨了大型语言模型（LLMs）在医学转录中提高自动语音识别（ASR）系统准确性的潜力，通过使用 PriMock57 数据集对 ASR 生成的转录进行改善，改进了一般词错误率（WER）、医学概念错误率（MC-WER）和音频流分离准确性，同时通过比较不同提示技术在日记化和纠错准确性上的有效性，发现 LLMs 特别是 Chain-of-Thought（CoT）提示技术不仅改善了现有 ASR 系统的日记化准确性，而且在医学转录领域取得了卓越表现，从而更准确地捕捉医学概念并提高转录对话的语义连贯性，这些发现表明 LLMs 在增强 ASR 输出的同时，也在转录任务中独立取得了显著成果，为改进医学 ASR 系统、提高医疗保健领域的病患记录的准确性和可靠性带来巨大的希望。