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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…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-0854· cite & verify
Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency
Hospital Universitari Doctor Peset, València 10 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

In brief

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 AI and with each of four AI algorithms.

AI assistance did not improve diagnostic accuracy. AI support reduced senior consultations in selected pairings and, in one case, lowered CT escalation.

Main points

  1. 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.
  2. 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 AI and with each of four AI algorithms.
  3. A 14-day washout period separated sessions, and case order was randomized.

The problem

These results suggest that to avoid automation bias, maintain accuracy, and achieve efficiency gains, AI deployment requires careful local adaptation.

The rundown

A 14-day washout period separated sessions, and case order was randomized. The reference standard was the finalized clinical report, supplemented by confirmatory CT when available.

Sources

  1. Peer-reviewedAcademic Radiology2026-08-21

The debate