mlsanityData to 7 Oct 2026

The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models

arXiv 2402.07658 ↗First posted 12 Feb 2024cs.CL cs.SD eess.AScited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 33 of its 59 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

33 of 59 references.

References · 33

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21 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 3; 2026: 8.

17 papers.

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  1. 2024LLM-based speaker diarization correction: A generalizable approachcited by 8
  2. 2024From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practicecited by 8
  3. 2024Performant ASR Models for Medical Entities in Accented Speechcited by 7
  4. 2024AG-LSEC: Audio Grounded Lexical Speaker Error Correctioncited by 3
  5. 2025Enhancing Dialogue Annotation with Speaker Characteristics Leveraging a Frozen LLMcited by 3
  6. 2024SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object Localizationcited by 2
  7. 2025SEAL: Speaker Error Correction using Acoustic-conditioned Large Language Modelscited by 1
  8. 2026Lost in Transcription: How Speech-to-Text Errors Derail Code Understandingcited by 1
  9. 2024Searching for Best Practices in Medical Transcription with Large Language Modelcited by 0
  10. 2025Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generationcited by 0
  11. 2026Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarizationcited by 0
  12. 2026Evaluating a Multi-Agent Voice-Enabled Smart Speaker for Care Homes: A Safety-Focused Frameworkcited by 0
5 more
  1. 2026Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Datasetcited by 0
  2. 2026A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Taskscited by 0
  3. 2026Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysiscited by 0
  4. 2026Mitigating Speaker Leakage in Cascaded Multi-talker ASR with Diarization-based Transcript Correctioncited by 0
  5. 2026MedWER: A Reproducible, Model-Free Evaluation Protocol for Medical Speech Recognitioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Systematic Analysis of Large Language Models with RAG-enabled Dynamic Prompting for Medical Error Detection and Correction
  2. 2026MedSpeak: A Knowledge Graph-Aided ASR Error Correction Framework for Spoken Medical QA
  3. 2024MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation
  4. 2023Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model
  5. 2024Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?
  6. 2024The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models
  7. 2025MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
  8. 2023An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing