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Algorithmic transparency and interpretability measures improve radiologists' performance in BI-RADS 4 classification.

Friederike Jungmann1, Sebastian Ziegelmayer1, Fabian K Lohoefer1

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Artificial intelligence significantly improved radiologists' diagnostic accuracy for BI-RADS 4 lesions. Trust in AI was driven by prediction certainty and plausible heatmaps, with personality influencing human-AI collaboration.

Keywords:
AlgorithmsArtificial intelligencePerceptionRadiologistsTrust

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • Breast cancer screening relies on accurate interpretation of mammography findings.
  • Classifying Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions presents diagnostic challenges.
  • Artificial intelligence (AI) offers potential to enhance diagnostic performance.

Purpose of the Study:

  • To evaluate how different AI-based assistance types affect radiologists' perception and interaction.
  • To assess the impact of AI predictions and certainty measures on diagnostic performance.
  • To explore the influence of radiologist personality traits on human-AI collaboration.

Main Methods:

  • Retrospective observer study involving four radiologists classifying BI-RADS4 lesions (101 benign, 99 malignant).
  • Assessed the effect of AI assistance (interpretability map, classification, certainty) on radiologist performance (sensitivity, specificity, questionnaire).
  • Analyzed the correlation between Big Five personality traits and AI interaction using Pearson correlation.

Main Results:

  • AI-based assistance significantly improved diagnostic accuracy (2.8% increase, p=0.045).
  • Radiologist trust in AI was primarily driven by prediction certainty (100% agreement).
  • Observed varied human-AI interactions; high neuroticism correlated with persuasibility (r=0.98, p=0.02).

Conclusions:

  • AI assistance enhances diagnostic accuracy for BI-RADS 4 mammography lesions.
  • Trust in AI performance depends on prediction certainty and plausible heatmaps.
  • Personality traits influence human-AI collaboration, affecting classification changes based on AI predictions.