Clinical speech recognition models like Whisper and its enhanced variant WhisperX struggle with non-native English accents, leading to higher transcription errors in medical settings. In a pilot study, researchers recorded four standardized clinical scripts—ranging from a gastroenterologist’s colonoscopy explanation to inter-physician histories from cardiology, gastroenterology, and pulmonology—and ran them through both ASR systems. Word Error Rates (WER) and Levenshtein distances were markedly worse for non-native speakers; Whisper’s gap was larger, but WhisperX still showed significant disparity. A specialized biomedical NER model revealed that complex terms in the pulmonology and cardiology texts suffered most, dropping precision and F1 scores compared to simpler scenarios. Beyond raw error counts, semantic measures told a similar story. WhisperX maintained strong BERTScores across all texts—indicating overall meaning was often preserved despite mistakes—but non-native segments still saw degraded scores. To tackle this, the team piped WhisperX outputs into GPT-4o for post-processing. That chained pipeline slashed accent-related errors, restoring parity between native and non-native transcripts and improving NER recall by notable margins. The paper reports that GPT-4o corrections brought non-native WER back in line with native speaker performance, though exact numbers vary by script complexity. These findings matter because nearly one in five U.S. healthcare workers is foreign-born and increasingly relies on AI scribes for real-time documentation. Left unchecked, ASR bias could introduce clinical risk through misrecorded diagnoses or medication names. By combining a top-tier ASR engine with a powerful LLM for correction, the study offers a concrete approach to leveling the playing field across diverse accents—a step toward safer, more equitable AI tools in medicine.