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papersTODAY 04:00 UTC

Study compares end-to-end models for clinical SOAP note generation from audio

A new arXiv paper examines how well audio-language models can turn long doctor-patient conversations into structured SOAP clinical notes. The authors compare lightweight and heavyweight end-to-end approaches, noting that while cascaded speech recognition pipelines remain strong, end-to-end models tend to lose information or produce hallucinations. The work targets the modality gap in long-form clinical audio.

arXivSOAP notesai-hallucinationaudio-language-modelsclinical documentationspeech-recognition

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