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AI Guides Radiologists' Attention to Lesions Without Slowing Mammogram Reviews

Eye-tracking shows AI decision support directs radiologists to suspicious regions without extending review times.

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Overview

  • In a July reader study, 12 radiologists reviewed 150 screening mammograms with and without AI assistance to map gaze patterns and diagnostic performance.
  • AI assistance improved sensitivity from 81.7% to 87.2% and specificity from 89.0% to 91.1%, producing a significant AUC rise from 0.93 to 0.97.
  • Eye-tracking data showed radiologists fixated on true lesion regions for 5.4 seconds with AI guidance versus 4.4 seconds without and reduced normal-area coverage from 11.1% to 9.5%.
  • Radiologists modulated their review pace based on AI suspicion scores, fast-tracking low-risk cases and conducting deeper analyses of high-suspicion examinations.
  • Researchers are launching follow-up studies to optimize alert timing and develop uncertainty-prediction tools that aim to maximize benefits and reduce the risk of overreliance.