Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48496, first published .
A Comprehensive, Valid, and Reliable Tool to Assess the Degree of Responsibility of Digital Health Solutions That Operate With or Without Artificial Intelligence: 3-Phase Mixed Methods Study

A Comprehensive, Valid, and Reliable Tool to Assess the Degree of Responsibility of Digital Health Solutions That Operate With or Without Artificial Intelligence: 3-Phase Mixed Methods Study

A Comprehensive, Valid, and Reliable Tool to Assess the Degree of Responsibility of Digital Health Solutions That Operate With or Without Artificial Intelligence: 3-Phase Mixed Methods Study

Journals

  1. Weimar S, Martjan R, Terzidis O. Conceptualizing the landscape of digital health entrepreneurship: a systematic review and research agenda. Management Review Quarterly 2025;75(2):1619 View
  2. Alami H, Lehoux P, E. Shaw S, Niang M, Malas K, Fortin J. To What Extent Can Digital Health Technologies Comply With the Principles of Responsible Innovation? Practice-and Policy-Oriented Research Insights Regarding an Organisational and Systemic Issue. International Journal of Health Policy and Management 2024;13:8061 View
  3. Jacob C, Brasier N, Laurenzi E, Heuss S, Mougiakakou S, Cöltekin A, Peter M. AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis. Journal of Medical Internet Research 2025;27:e67485 View

Conference Proceedings

  1. Singh S. 2023 International Conference on Computational Science and Computational Intelligence (CSCI). Leadership Challenges and Strategies in the Era of AI Transformation View