LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It
Ambient AI scribes draft clinical notes, and published audits find their dominant error is omission: information the encounter established that the note fails to record. The standard check is an LLM judge: a second model reads the note against the transcript and flags problems. We ask whether judges detect omissions. Public corpora cannot supply the answer key: their clinician reference notes and transcripts are materially discrepant. Our benchmark has 500 single-error note pairs from audited fact sheets, 298 with a named fact certainly absent and 202 added-or-altered controls. Across eight ju
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- LinkedLinked via arxiv author · 85%Sebastian Fox →
“LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It”
- LinkedLinked via arxiv author · 85%Luke Markham →
“LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It”
- LinkedLinked via arxiv author · 85%Ryan Lail →
“LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It”