The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors Across Borders?
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866916584325382144 |
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| author | Adedeji, Ayo Sanni, Mardhiyah Ayodele, Emmanuel Joshi, Sarita Olatunji, Tobi |
| author_facet | Adedeji, Ayo Sanni, Mardhiyah Ayodele, Emmanuel Joshi, Sarita Olatunji, Tobi |
| contents | The global adoption of Large Language Models (LLMs) in healthcare shows promise to enhance clinical workflows and improve patient outcomes. However, Automatic Speech Recognition (ASR) errors in critical medical terms remain a significant challenge. These errors can compromise patient care and safety if not detected. This study investigates the prevalence and impact of ASR errors in medical transcription in Nigeria, the United Kingdom, and the United States. By evaluating raw and LLM-corrected transcriptions of accented English in these regions, we assess the potential and limitations of LLMs to address challenges related to accents and medical terminology in ASR. Our findings highlight significant disparities in ASR accuracy across regions and identify specific conditions under which LLM corrections are most effective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors Across Borders? Adedeji, Ayo Sanni, Mardhiyah Ayodele, Emmanuel Joshi, Sarita Olatunji, Tobi Computation and Language Sound Audio and Speech Processing The global adoption of Large Language Models (LLMs) in healthcare shows promise to enhance clinical workflows and improve patient outcomes. However, Automatic Speech Recognition (ASR) errors in critical medical terms remain a significant challenge. These errors can compromise patient care and safety if not detected. This study investigates the prevalence and impact of ASR errors in medical transcription in Nigeria, the United Kingdom, and the United States. By evaluating raw and LLM-corrected transcriptions of accented English in these regions, we assess the potential and limitations of LLMs to address challenges related to accents and medical terminology in ASR. Our findings highlight significant disparities in ASR accuracy across regions and identify specific conditions under which LLM corrections are most effective. |
| title | The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors Across Borders? |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.15310 |