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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.03797 |
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| _version_ | 1866913533476732928 |
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| author | Li, Jiafeng Mu, Yanda |
| author_facet | Li, Jiafeng Mu, Yanda |
| contents | The transcription of medical monologues, especially those containing a high density of specialized terminology and delivered with a distinct accent, presents a significant challenge for existing automated systems. This paper introduces a novel approach leveraging a Large Language Model (LLM) to generate highly accurate medical transcripts from audio recordings of doctors' monologues, specifically focusing on Indian accents. Our methodology integrates advanced language modeling techniques to lower the Word Error Rate (WER) and ensure the precise recognition of critical medical terms. Through rigorous testing on a comprehensive dataset of medical recordings, our approach demonstrates substantial improvements in both overall transcription accuracy and the fidelity of key medical terminologies. These results suggest that our proposed system could significantly aid in clinical documentation processes, offering a reliable tool for healthcare providers to streamline their transcription needs while maintaining high standards of accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03797 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Searching for Best Practices in Medical Transcription with Large Language Model Li, Jiafeng Mu, Yanda Computation and Language The transcription of medical monologues, especially those containing a high density of specialized terminology and delivered with a distinct accent, presents a significant challenge for existing automated systems. This paper introduces a novel approach leveraging a Large Language Model (LLM) to generate highly accurate medical transcripts from audio recordings of doctors' monologues, specifically focusing on Indian accents. Our methodology integrates advanced language modeling techniques to lower the Word Error Rate (WER) and ensure the precise recognition of critical medical terms. Through rigorous testing on a comprehensive dataset of medical recordings, our approach demonstrates substantial improvements in both overall transcription accuracy and the fidelity of key medical terminologies. These results suggest that our proposed system could significantly aid in clinical documentation processes, offering a reliable tool for healthcare providers to streamline their transcription needs while maintaining high standards of accuracy. |
| title | Searching for Best Practices in Medical Transcription with Large Language Model |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.03797 |