User: Guest  Login
Title:

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

Document type:
Journal Article
Author(s):
Jungmann, Friederike; Ziegelmayer, Sebastian; Lohoefer, Fabian K; Metz, Stephan; Müller-Leisse, Christina; Englmaier, Maximilian; Makowski, Marcus R; Kaissis, Georgios A; Braren, Rickmer F
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 radiologist...     »
Journal title abbreviation:
Eur Radiol
Year:
2023
Journal volume:
33
Journal issue:
3
Pages contribution:
1844-1851
Fulltext / DOI:
doi:10.1007/s00330-022-09165-9
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/36282311
Print-ISSN:
0938-7994
TUM Institution:
Institut für Diagnostische und Interventionelle Radiologie ; Institut für KI und Informatik in der Medizin
 BibTeX