aceA Hybrid Human-AI Workflow for Ultrasound Report Quality Control: A Multicenter Performance and Efficiency Evaluation
Rationale and objectives: This study evaluated a hybrid human-AI workflow for ultrasound report quality assurance using multicenter retrospective data. Materials and methods: 786 anonymized ultrasound reports covering 22 examination categories were collected from four hospitals. Reports containing naturally occurring or implanted errors were matched across arms. GPT-4o and DeepSeek-v3 performed initial screening before review by six human reviewers (HRs). HR with blinded expert over-reading (HR-ex) served as con…
Pairing GPT-4o or DeepSeek-v3 screening with human review matched single-reviewer accuracy on ultrasound reports while cutting median audit time from 51.0 s to about 11 to 12 s.
The study was retrospective and the authors state that prospective real-world validation is required before routine clinical implementation; the hybrid also trailed expert over-reading on accuracy and false negatives.
Evidence
- Peer-reviewedAcademic Radiology2026-10-17
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Truvace Impact Record TRV-2026-1805, v1: “A Hybrid Human-AI Workflow for Ultrasound Report Quality Control: A Multicenter Performance and Efficiency Evaluation.” Truvace, 2026-10-18. /record/TRV-2026-1805 (accessed at citation time). sha256 01300edaa7cea936…
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