Fostering Trust in AI driven Healthcare
A framework for responsible generative AI
Machine learning (ML) offers transformative opportunities for healthcare, with applications ranging from precision medicine to operational optimization. However, progress is constrained by limited access to diverse, high-quality datasets, exacerbated by fragmentation, data scarcity, and stringent privacy regulations. Traditional data augmentation methods fail to fully capture the complexity and heterogeneity of healthcare data. Generative AI, particularly large language models (LLMs), offers a promising alternative by synthesizing realistic datasets while addressing data scarcity. Yet, their adoption in healthcare is hindered by critical concerns about trustworthiness, including semantic validity, fairness, bias mitigation, fidelity, privacy preservation, and real-world utility.
Overall Objective
THUNDER aims to forge a comprehensive framework for trustworthy and responsible generative AI in healthcare
Our Consortium
A Pan-European and International Collaboration
154 Secondments
Bringing together leading universities, research institutions, and industry partners across Europe and associated countries.