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Fostering Trust in AI driven Healthcare: SecUre and uNbiased knowleDge guided gEneRative AI

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Fostering Trust in AI driven Healthcare: SecUre and uNbiased knowleDge guided 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. This research identifies key gaps in developing trustworthy generative models and ML methods for

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recordType

award

status

SIGNED

region

EU

value

771540

unit

EUR