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

Theme Issue (2023): Generative & Multimodal AI in Digital Cardiovascular Medicine (8) Clinical Informatics (1327) Clinical Information and Decision Making (1927) Decision Support for Health Professionals (1548) Machine Learning (2081) Artificial Intelligence (2141) Computer-Aided Diagnosis (252) E-Health / Health Services Research and New Models of Care (721)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44732, first published .
Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning–Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders

Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning–Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders

Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning–Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders

Authors of this article:

Vy Ho1 Author Orcid Image ;   Cati Brown Johnson2 Author Orcid Image ;   Ilies Ghanzouri1 Author Orcid Image ;   Saeed Amal3 Author Orcid Image ;   Steven Asch2, 4 Author Orcid Image ;   Elsie Ross1 Author Orcid Image
Article Authors Cited by (18) Tweetations (6) Metrics

Journals

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Conference Proceedings

  1. Sangeetha A, Malladi S. 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom). A Survey on Early Heart Disease Prediction using Machine Learning Techniques View