Radiologists’ performance improved by algorithmic transparency and interpretability measures

This study’s aim was to evaluate the perception of various types of artificial intelligence-based assistance and the interaction of radiologists with the algorithm’s predictions and certainty measures. As was consistent with previous research, the authors determined that human performance was superior to both groups when combined with AI. They also found an increase in trust in the algorithm’s performance when displaying aspects of the classification process of the algorithm.

Key points

• AI-based assistance significantly improved the diagnostic accuracy of radiologists in classifying BI-RADS 4 mammography lesions.
• Trust in the algorithm’s performance was mostly dependent on the certainty of the prediction in combination with a reasonable heatmap.
• Personality traits seem to influence human-AI collaboration. Radiologists with specific personality traits were more likely to change their classification according to the algorithm’s prediction than others.

Article: Algorithmic transparency and interpretability measures improve radiologists’ performance in BI-RADS 4 classification

Authors: Friederike Jungmann, Sebastian Ziegelmayer, Fabian K. Lohoefer, Stephan Metz, Christina Müller-Leisse, Maximilian Englmaier, Marcus R. Makowski, Georgios A. Kaissis & Rickmer F. Braren