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ARXIV:2604.01705 · MEDICAL AI · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01705MEDICAL AISUBMITTED 03 APR · 20:50 UTCFRESHNESS STALERuijie Yang · Yan Zhu · Peiyao Fu · Te Luo · Zhihua Wang · Xian Yang · +3 at arXiv
A domain-adapted speech recognition system for real-time human-AI collaboration in gastrointestinal endoscopy, significantly improving accuracy and enabling efficient edge deployment.
Opportunity summary
Pain A domain-adapted speech recognition system for real-time human-AI collaboration in gastrointestinal endoscopy, significantly improving accuracy and enabling efficient edge deployment.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A domain-adapted speech recognition system for real-time human-AI collaboration in gastrointestinal endoscopy, significantly improving accuracy and enabling efficient edge deployment. Here, we present EndoASR, a domain-adapted ASR system designed for real-time deployment in endoscopic…
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,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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%…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A domain-adapted speech recognition system for real-time human-AI collaboration in gastrointestinal endoscopy, significantly improving accuracy and enabling efficient edge deployment.
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10.48550/arXiv.2604.01705A domain-adapted speech recognition system for real-time human-AI collaboration in gastrointestinal endoscopy, significantly improving accuracy and enabling efficient edge deployment.
Abstract
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.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
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Viability
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Dimensions overall score 7.0
PROBLEM
A domain-adapted speech recognition system for real-time human-AI collaboration in gastrointestinal endoscopy, significantly improving accuracy and enabling efficient edge deployment. Here, we present EndoASR, a domain-adapted ASR system designed for real-time deployment in endo...
METHOD
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-ad...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 increa...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
reducing character error rate (CER) from 20.52% to 14.14%
Directly stated in abstract with specific numeric results
partial
increasing medical term accuracy (Med ACC) from 54.30% to 87.59%
Directly stated in abstract with specific numeric results
partial
CER is reduced from 16.20% to 14.97%
Directly stated in abstract with specific numeric results from multi-center study
partial
Med ACC is improved from 61.63% to 84.16%
Directly stated in abstract with specific numeric results from multi-center study
partial
EndoASR achieves a real-time factor (RTF) of 0.005, significantly faster than Whisper-large-v3 (RTF 0.055)
Directly stated in abstract with comparative numeric results
partial
maintaining a compact model size of 220M parameters, enabling efficient edge deployment
Directly stated in abstract with specific parameter count and deployment implication
partial
integration with large language models demonstrates that improved ASR quality directly enhances downstream structured information extraction and clinician-AI interaction
Directly stated in abstract but requires some inference about the causal relationship
partial
We develop a two-stage adaptation strategy based on synthetic endoscopy reports, targeting domain-specific language modeling and noise robustness
Directly stated in abstract describing the method development
partial
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A domain-adapted speech recognition system for real-time human-AI collaboration in gastrointestinal endoscopy, significantly improving accuracy and enabling efficient edge deployment.
Segment
Medical AI
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Commercial read
7.0/10 public viability
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reason
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proof status
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confidence low
next verification path
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stale
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Technical feasibility
partial
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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DEFENSIBILITY
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