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

Formative Evaluation of Digital Health Interventions (2823) Research Instruments, Questionnaires, and Tools (665) Imaging Informatics (235) New Methods (1030) mHealth for Data Collection and Research (1082) Clinical Information and Decision Making (1656) Machine Learning (1738) Respiratory Medicine (122) Artificial Intelligence (1740)

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
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Journals

  1. Chen H, Cohen E, Alfred M. Examining the development, effectiveness, and limitations of computer-aided diagnosis systems for retained surgical items detection: a systematic review. Ergonomics 2025:1 View