Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency
To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. In this prospective, monocentric, crossover reader study, five readers (one to six years of experience) assessed 1200 consecutive patients undergoing chest radiography (1861 total radiographs) for pulmonary infiltrates, pleural effusions, mediastinal masses, pneumothorax, and pulmonary nodules under five conditions: without…
These results suggest that to avoid automation bias, maintain accuracy, and achieve efficiency gains, AI deployment requires careful local adaptation.
Evidence
- Peer-reviewedAcademic Radiology2026-08-21
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Truvace Impact Record TRV-2026-0854, v1: “Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency.” Truvace, 2026-08-23. /record/TRV-2026-0854 (accessed at citation time). sha256 27fd0f6b0c3aef26…
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