Published on in Vol 11 (2026)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/79166, first published
.
Privacy-Preserving Collaborative Diabetes Prediction in Heterogeneous Health Care Systems: Algorithm Development and Validation of a Secure Federated Ensemble Framework
Privacy-Preserving Collaborative Diabetes Prediction in Heterogeneous Health Care Systems: Algorithm Development and Validation of a Secure Federated Ensemble Framework
Journals
- Sowan B, Massad B. Federated learning in obstetrics and gynaecology: A systematic review of applications, architectures, privacy frameworks, and clinical translation challenges. Informatics in Medicine Unlocked 2026;65:101794 View
- Sun Y, Yang R, Xin Q, Luo X, Zhou Y, Fu Z, Li J, Gu Z, Liu J, Wang Q, Yang Y. Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management. Frontiers in Endocrinology 2026;17 View
- Lucas I, Sousa J, Vitorino C. Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks. The AAPS Journal 2026;28(6) View
Conference Proceedings
- Karthikeyan L, Hazaimeh Y, Venkatraman A, M R, S D. 2026 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). AI-Driven Personalized Medicine: Risk Prediction and Tailored Therapies Using Wearable and Genomic Data View
- Awotunde J, Adeniyi A, Aabdulraheem M, Partal T, Randle O. 2026 10th International Artificial Intelligence and Data Processing Symposium (IDAP). An Ensemble Deep Federated Learning Privacy-Preserving Enabled with Secure Aggregation and Differential Privacy in Smart Healthcare Systems View