OpenAI Whisper hallucinating medical transcriptions — fabricated diagnoses in healthcare AI
Date: 2024-10
Affected: 30,000+ healthcare providers using Whisper-based transcription — patient records
Attack vector: hallucination
Category: research-demonstrated
Severity: Critical

Researchers at the University of Michigan found that OpenAI's Whisper speech-to-text model, widely used in healthcare for medical transcription, hallucinated content in approximately 1% of transcriptions. Hallucinations included fabricated medical diagnoses, medication names, and patient statements that never occurred. The problem was particularly severe in recordings with pauses, accents, or background noise. Over 30,000 healthcare providers were estimated to use Whisper-based transcription tools.

Impact: Fabricated medical diagnoses in patient records; patient safety risk; potential malpractice liability; FDA scrutiny of AI medical devices

Mitigations:
Human review requirement for all AI-generated medical transcriptions
Confidence scoring with mandatory manual review below threshold
Hallucination detection for medical terminology and clinical assertions
FDA pre-market review for AI systems generating medical records

Architectural layers (MAESTRO):
label: Foundation Models
notes: Speech-to-text model generates plausible but fabricated medical content during transcription gaps
role: origin
label: Data Operations
notes: Fabricated medical information enters patient records as 'transcribed' content
role: impact
label: Evaluation & Observability
notes: Hallucination rate testing insufficient for safety-critical medical deployment
role: blind-spot