The Sound of Healthcare: Improving Medical Transcription ASR Accuracy
  with Large Language Models
Ayo Adedeji Sarita Joshi Brendan Doohan
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.