GLEAM

GLEAM (Green Learning for Eco-Conscious Medical AI) develops a modular, energy-efficient, and privacy-preserving AI framework for healthcare. The project addresses two major challenges in medical AI: the growing environmental impact of computation and the strict privacy regulations that limit cross-institutional collaboration. By combining Green AI and Privacy-by-Design principles, GLEAM enables hospitals and research institutions to train and deploy trustworthy AI models with minimal energy use and full data sovereignty. The framework integrates TinyML, Federated Learning, Synthetic Data Generation, and Energy Benchmarking into a unified system for sustainable and compliant AI development. Technologies such as hardware-aware optimization, dynamic workload scheduling, and post-quantum cryptography ensure efficient and secure operation across diverse medical infrastructures. Built on FHIR and DICOM standards, GLEAM connects seamlessly with existing clinical workflows. Pilots across multiple countries will validate the framework’s sustainability, privacy compliance, and clinical value—demonstrating that advanced medical AI can be both trustworthy and environmentally responsible by design.