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

Viewpoints and Perspectives (1456) Clinical Information and Decision Making (3867) Theoretical Frameworks and Concepts (580) Business and Entrepreneurship in eHealth (70) Machine Learning (3318) Artificial Intelligence (4960) Responsible Health AI (254)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/80028, first published .
Doctor using laptop with medical icons for digital health technology

Scalable Agile Framework for Execution in AI for Medical AI Ethics Policy Design in Small- and Medium-Sized Enterprises

Scalable Agile Framework for Execution in AI for Medical AI Ethics Policy Design in Small- and Medium-Sized Enterprises

Authors of this article:

Ion Nemteanu1 Author Orcid Image ;   Adir Mancebo Jr2 Author Orcid Image ;   Leslie Joe2 Author Orcid Image ;   Ryan Lopez2 Author Orcid Image ;   Patricia Lopez2 Author Orcid Image ;   Warren Woodrich Pettine3, 4 Author Orcid Image
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Journals

  1. Sato M, Nagata S, Ohnuma M, Takahashi H, Kakazu T, Yamamura M, Yoshikawa A, Matsushita Y. A Locally Executable AI System for Improving Preoperative Patient Communication: Multidomain Clinical Evaluation. JMIR Medical Informatics 2026;14:e89173 View
  2. Li J, Hu Y, Jin Y, Ji S, Lu Z, Cheng X, Feng C, Chen X. Intelligent biointerfaces for hemostasis and wound repair: The evolution from passive coverage to closed-loop intervention. Chemical Engineering Journal 2026;549:181468 View
  3. Altamirano S, Anadria D, van der Wees S, Tensen P, van de Vijver S, Ghebreab S. Translating algorithmic fairness into health practice: formative evaluation of the fairness-to-action framework. Frontiers in Artificial Intelligence 2026;9 View