用大模型纠正不同地区医疗语音识别错误，提升跨文化诊疗安全
The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors Across Borders?
- 对比尼日利亚、英国、美国的医疗语音识别误差，测试大模型纠错效果
- 发现不同地区识别准确率差异显著，大模型在特定语境下纠错效果突出
- 适合关注医疗AI跨区域应用与语音识别优化的研究者参考
大型语言模型（LLMs）在医疗领域的全球应用展现出提升临床流程和患者预后的潜力。然而，关键医学术语的自动语音识别（ASR）错误仍是重大挑战，若未被发现可能危及患者安全。本研究调查了尼日利亚、英国和美国医疗转录中ASR错误的普遍性与影响。通过评估这些地区带有口音的英语在原始转录和经大模型修正后的表现，我们评估了大模型在应对口音与医学术语挑战方面的潜力与局限。研究结果揭示了各地区间显著的ASR准确性差异，并明确了大模型纠错最有效的具体条件。
原文摘要 · Abstract (English)
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.
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