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

Formative Evaluation of Digital Health Interventions (5286) Tools, Programs and Algorithms (597) Decision Support for Health Professionals (2296) Ontologies, Classifications, and Coding (460) Innovations and Technology for Cancer Prevention and Screening (270) Imaging Informatics (488) Machine Learning (3287) Artificial Intelligence (4912) Cancer Prognosis Models and Machine Learning (142) Colon and Rectal Cancer (84) AI applications in genomics and pathology informatics, including generative AI (45)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67457, first published .
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Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Authors of this article:

David Beyer1 Author Orcid Image ;   Evan Delancey2 Author Orcid Image ;   Logan McLeod3 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Qin Y, Chen X, Wang Y, An Y. HSF-Net: Hybrid spatial and frequency domain transformer network for polyp segmentation. Neurocomputing 2026;664:132100 View
  2. Shahriar M, Hafiz M, Islam M, Mridha M. FedPAXA-Net: Prototype-aligned personalized federated learning for multi-client gastrointestinal endoscopy image classification. Intelligence-Based Medicine 2026;16:100472 View
  3. Hamdan M, Badr Z, Uribe S, Reymus M, Kalenderian E, Schwendicke F. No-Code and Low-Code Artificial Intelligence in Healthcare Imaging: A Scoping Review. International Dental Journal 2026;76(6):109797 View