DECIDE-Twin: A Framework for AI-Enabled Digital Twins in Clinical Decision-Making
We developed the DECIDE-Twin framework that could serve as a guide for researchers and practitioners in DT development and implementation for clinical decision-making. Further research is needed to validate and implement this framework for various clinical applications.
Authors
- Farbod Khameneifar (ORCID: https://orcid.org/0000-0001-5492-2295)
- Samira Abbasgholizadeh Rahimi (ORCID: https://orcid.org/0000-0003-3781-1360)
- Amalia M. Issa (ORCID: https://orcid.org/0000-0002-1667-3280)
- Ashkan Baradaran (ORCID: https://orcid.org/0000-0002-7982-8032)
- Geneviève Gore (ORCID: https://orcid.org/0000-0003-1072-2683)
Institutions
- Polytechnique Montréal (CA)
- McGill University (CA)
Publication Details
- Journal
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1109/jbhi.2024.3521717
- Citations
- 12
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.44