This paper is in the following e-collection/theme issue:

Applications of AI (104) Artificial Intelligence (1421) AI Data (13)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52211, first published .
The Impact of Expectation Management and Model Transparency on Radiologists’ Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study

The Impact of Expectation Management and Model Transparency on Radiologists’ Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study

The Impact of Expectation Management and Model Transparency on Radiologists’ Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study

Authors of this article:

Lotte J S Ewals1 Author Orcid Image ;   Lynn J J Heesterbeek2 Author Orcid Image ;   Bin Yu3 Author Orcid Image ;   Kasper van der Wulp1 Author Orcid Image ;   Dimitrios Mavroeidis4 Author Orcid Image ;   Mathias Funk5 Author Orcid Image ;   Chris C P Snijders6 Author Orcid Image ;   Igor Jacobs7 Author Orcid Image ;   Joost Nederend1 Author Orcid Image ;   Jon R Pluyter2 Author Orcid Image ;   e/MTIC Oncology group8
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Chen H, Alfred M, Brown A, Atinga A, Cohen E. Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study. JMIR Formative Research 2024;8:e59045 View