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

Formative Evaluation of Digital Health Interventions (3272) Applications of AI (295) Computer-Aided Diagnosis (255) Artificial Intelligence (2162) Quality/Credibility of eHealth Information and Trust Issues (657)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58666, first published .
Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

Authors of this article:

Tetsu Sakamoto1 Author Orcid Image ;   Yukinori Harada1 Author Orcid Image ;   Taro Shimizu1 Author Orcid Image
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  1. Pan X, Wang C, Luo X, Dong Q, Sun H, Zhang W, Qu H, Deng R, Lin Z. Development and verification of a convolutional neural network-based model for automatic mandibular canal localization on multicenter CBCT images. BMC Oral Health 2025;25(1) View

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  1. Correia A, Fonseca B, Schneider D, Chaves R, Kärkkäinen T. 2025 9th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). Uncertainties and Emerging Uses of Human-Ai Medical Diagnosis in Collaborative Clinical Practice View