Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72411, first published .
Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint

Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint

Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint

Authors of this article:

Alexandre Vallée1 Author Orcid Image

Journals

  1. Vallée A, Feki A, Moawad G, Ayoubi J. A semi-mechanistic mathematical framework for simulating multi-hormone dynamics in reproductive endocrinology. Computational and Structural Biotechnology Journal 2025;27:3654 View
  2. Gillgallon R, Bergami G, Morgan G. Federated Load Balancing in Smart Cities: A 6G, Cloud, and Agentic AI Perspective. Applied Sciences 2025;15(20):10920 View
  3. Zhang W, Lin H, Luan M, Wei P, Han Y. Digital vessel for the diagnosis of cardiovascular diseases. Vessel Plus 2025 View
  4. Piechowiak M, Goch A, Panas E, Masiak J, Mikołajewski D, Rojek I, Mikołajewska E. The Global Importance of Machine Learning-Based Wearables and Digital Twins for Rehabilitation: A Review of Data Collection, Security, Edge Intelligence, Federated Learning, and Generative AI. Electronics 2025;14(23):4699 View
  5. Belančić A, Štimac I, Faour A, Gkrinia E. Digital twins: Unlocking comparative evidence that clinical research urgently needs. British Journal of Clinical Pharmacology 2025 View
  6. Shahrezaei A, Taherkhani S, Dashti L, Garmaroodi G, Nasirinezhad F. Herbal medicine meets machine learning: a systematic review of AI-powered innovation in chronic inflammation management. Discover Applied Sciences 2025 View
  7. Piechowiak M, Goch A, Panas E, Masiak J, Mikołajewski D, Rojek I, Mikołajewska E. From Local to Global Perspective in AI-Based Digital Twins in Healthcare. Applied Sciences 2025;16(1):83 View