Good news! Impressive! Errors in translation could be deadly!
When will all and any patient doctor encounters be quickly transcribed?
"Copenhagen-based Corti launched Symphony for Speech-to-Text, a clinical-grade recognition model that achieved a 1.4 percent word error rate on English medical terminology—versus OpenAI’s 17.7 percent, ElevenLabs’ 18.1 percent, Whisper’s 17.4 percent, and Parakeet’s 18.9 percent. The gap widens further on structured clinical entities like medication dosages: Corti hit 98.3 percent recall while the strongest generalist model managed 44.3 percent. That difference matters more now than it used to.
As healthcare shifts toward autonomous AI agents making real-time clinical decisions, transcription errors compound—if a model mishears “hyperthyroidism” as “hypothyroidism,” every downstream system operates on corrupted data. Corti also outperformed legacy incumbent Dragon Medical One in dictation accuracy and now serves over 100 million patients annually across health systems including the UK’s National Health Service. ..."
From the abstract:
"After decades of use in dictation and, more recently, ambient documentation, speech is emerging as a primary modality for interacting with technology and AI in healthcare.
Yet medical speech recognition remains difficult: systems must capture specialized terminology, resolve contextual ambiguity, and render measurements, abbreviations, and clinical shorthand precisely.
Existing solutions are typically optimized either for general-purpose transcription or narrow dictation workflows, limiting their reliability in safety-critical settings and their usefulness for broader clinical workflows.
We introduce Symphony for Speech-to-Text, a medical-grade speech recognition system for real-time streaming and batch file-based clinical use. Symphony decomposes the transcription process into specialized components for recognition, formatting, and contextual correction to optimize medical term recall while producing clinically structured text in real time and adapting across use cases. Evaluations on public benchmark and medical speech datasets show that Symphony substantially outperforms state-of-the-art systems in clinical settings while matching or exceeding them in general-domain settings, suggesting robust generalization rather than overfitting.
We release a clinical benchmark dataset to support reliable validation and further progress in medical speech recognition. Symphony is available through a production-grade API for live dictation, conversational transcription, and batch audio file processing."
Corti's new Symphony for Speech-to-Text model beats OpenAI at medical terminology accuracy, highlighting the value of specialized AI
Symphony for Speech-to-Text: Behind the research supporting real-time medical voice interfaces (original news release)
Symphony for Speech-to-Text: Supporting Real-Time Medical Voice Interfaces (preprint, open access)
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