Yasaman Fatapour, Jamil S. Samaan, Nicholas P. Tatonetti, Aditi Kuchi, Apoorva Srinivasan, Sarvenaz Fatapour, Hongyu Liu, Jacob Berkowitz, Kevin K. Tsang, Michael Ziets, Nadine A. Friedrich, Nitin Srinivasan, Shehan Thangaratnam, Ryan C King, Ryan Czarny, Trini Nguyen, Yee Hui Yeo, Kim Hyunseok, Yi‐Te Lee, Nicha Wongjarupong, Arash Abiri
Accurate clinical documentation is essential for safe, effective patient care. AI tools powered by automatic speech recognition can streamline this process. Variable performance across speakers with diverse accents leads to transcription errors and clinical risk. In testing Whisper and WhisperX on native and non-native English clinical speech, error rates were significantly higher for non-native speakers. Post-processing with GPT-4o restored lost accuracy. This chained approach (WhisperX-GPT) reduced accent-related errors.