The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors Across Borders?
This addresses ASR errors in healthcare for global medical applications, but it is incremental as it evaluates existing LLMs on new data.
The study investigated ASR errors in medical transcription across Nigeria, the UK, and the US, finding significant regional disparities in accuracy and identifying conditions where LLM corrections are most effective.
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.