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

ABSTRACT:

Objective

To evaluate the perception of different types of AI-based assistance and the interaction of radiologists with the algorithm's predictions and certainty measures.

Methods

In this retrospective observer study, four radiologists were asked to classify Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions (n = 101 benign, n = 99 malignant). The effect of different types of AI-based assistance (occlusion-based interpretability map, classification, and certainty) on the radiologists' performance (sensitivity, specificity, questionnaire) were measured. The influence of the Big Five personality traits was analyzed using the Pearson correlation.

Results

Diagnostic accuracy was significantly improved by AI-based assistance (an increase of 2.8% ± 2.3%, 95 %-CI

SUBMITTER: Jungmann F 

PROVIDER: S-EPMC9935738 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature