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

Formative Evaluation of Digital Health Interventions (3336) Research Instruments, Questionnaires, and Tools (761) Imaging Informatics (317) New Methods (1152) mHealth for Data Collection and Research (1216) Clinical Information and Decision Making (1988) Machine Learning (2154) Respiratory Medicine (163) Artificial Intelligence (2229)

Published on in Vol 8 (2024)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59045, first published .
Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study

Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study

Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study

Authors of this article:

Hongbo Chen1 Author Orcid Image ;   Myrtede Alfred1 Author Orcid Image ;   Andrew D Brown2 Author Orcid Image ;   Angela Atinga3 Author Orcid Image ;   Eldan Cohen1 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

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  3. Min C, Lee D, Kwak J. Cross-Assessment & Verification for Evaluation (CAVe) Framework for AI Risk and Compliance Assessment Using a Cross-Compliance Index (CCI). Electronics 2026;15(2):307 View
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