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The authors introduce EndoASR, a domain-adapted automatic speech recognition (ASR) system for real-time use in gastrointestinal endoscopy, addressing the challenges of domain-specific terminology and noisy clinical environments. They employ a two-stage adaptation strategy using synthetic endoscopy reports to improve both language modeling and noise robustness. Evaluated across multiple centers, EndoASR significantly improves transcription accuracy (reducing CER from 16.20% to 14.97%) and medical term accuracy (increasing Med ACC from 61.63% to 84.16%) while maintaining a real-time factor of 0.005 and a compact model size.
Domain-specific speech recognition can be both accurate AND fast: EndoASR slashes error rates for endoscopy reports while running 10x faster than Whisper-large-v3.
Automatic speech recognition (ASR) is a critical interface for human-AI interaction in gastrointestinal endoscopy, yet its reliability in real-world clinical settings is limited by domain-specific terminology and complex acoustic conditions. Here, we present EndoASR, a domain-adapted ASR system designed for real-time deployment in endoscopic workflows. We develop a two-stage adaptation strategy based on synthetic endoscopy reports, targeting domain-specific language modeling and noise robustness. In retrospective evaluation across six endoscopists, EndoASR substantially improves both transcription accuracy and clinical usability, reducing character error rate (CER) from 20.52% to 14.14% and increasing medical term accuracy (Med ACC) from 54.30% to 87.59%. In a prospective multi-center study spanning five independent endoscopy centers, EndoASR demonstrates consistent generalization under heterogeneous real-world conditions. Compared with the baseline Paraformer model, CER is reduced from 16.20% to 14.97%, while Med ACC is improved from 61.63% to 84.16%, confirming its robustness in practical deployment scenarios. Notably, EndoASR achieves a real-time factor (RTF) of 0.005, significantly faster than Whisper-large-v3 (RTF 0.055), while maintaining a compact model size of 220M parameters, enabling efficient edge deployment. Furthermore, integration with large language models demonstrates that improved ASR quality directly enhances downstream structured information extraction and clinician-AI interaction. These results demonstrate that domain-adapted ASR can serve as a reliable interface for human-AI teaming in gastrointestinal endoscopy, with consistent performance validated across multi-center real-world clinical settings.
