In dAIbetes, we leverage federated learning to build a federated health data platform, creating the first internationally trained virtual twin models for type 2 diabetes. Our models integrate big data across various sources while ensuring privacy compliance. This innovative approach aims to personalize treatment outcome predictions, which currently lack precise guidelines, using data from about 800,000 patients globally.
Our goal is to improve prediction accuracy by at least 10% over standard models, paving the way for personalized management of diabetes and other complex diseases. Our consortium brings together experts in AI, software, privacy, and diabetes treatment to address the crucial balance between data privacy and medical research needs.