Beyond Speech-to-Text: Preserving Meaning in Clinical Transcription

abstract

This research essay examines AI-assisted clinical transcription as a problem of meaning preservation rather than speech recognition alone. A transcript can read perfectly and still be clinically wrong: "no fracture" becomes "fracture", a decimal shifts, left becomes right, a small finding disappears. The essay sets out what has to survive the journey from dictation to report — findings and negation, anatomy and laterality, numbers and units, uncertainty, and report structure — and proposes a human-in-the-loop workflow in which AI prepares and checks a draft while the physician keeps final validation and release authority. It develops a controlled semantic post-processing layer that corrects recurring ASR errors where the source supports the correction and flags them where it does not, a risk-based review taxonomy that directs attention without letting the system decide clinical truth, and the surrounding platform — workflow states, versioned report models, audit trail — that makes any of it accountable. The work combines solution design with early qualitative exploration; it is explicitly not a completed clinical-validation study, and it states where that boundary falls.

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