  Secure data management and effective data sharing have become paramount in
the rapidly evolving healthcare landscape. The advancement of generative
artificial intelligence has positioned Multi-modal Large Language Models
(MLLMs) as crucial tools for managing healthcare data. MLLMs can support
multi-modal inputs and generate diverse types of content by leveraging
large-scale training on vast amounts of multi-modal data. However, critical
challenges persist in developing medical MLLMs, including healthcare data
security and freshness issues, affecting the output quality of MLLMs. In this
paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered
medical MLLMs framework for healthcare data management. This framework
leverages a hierarchical cross-chain architecture to facilitate secure data
training. Moreover, it enhances the output quality of MLLMs through hybrid RAG,
which employs multi-modal metrics to filter various unimodal RAG results and
incorporates these retrieval results as additional inputs to MLLMs.
Additionally, we employ age of information to indirectly evaluate the data
freshness impact of MLLMs and utilize contract theory to incentivize healthcare
data holders to share fresh data, mitigating information asymmetry in data
sharing. Finally, we utilize a generative diffusion model-based reinforcement
learning algorithm to identify the optimal contract for efficient data sharing.
Numerical results demonstrate the effectiveness of the proposed schemes, which
achieve secure and efficient healthcare data management.
