Hybrid RAG-empowered Multi-modal LLM for Secure Healthcare Data Management: A Diffusion-based Contract Theory Approach

• URL: http://arxiv.org/abs/2407.00978v1
• Date: Mon, 1 Jul 2024 05:28:40 GMT
• Title: Hybrid RAG-empowered Multi-modal LLM for Secure Healthcare Data Management: A Diffusion-based Contract Theory Approach
• Authors: Cheng Su, Jinbo Wen, Jiawen Kang, Yonghua Wang, Hudan Pan, M. Shamim Hossain,
• Abstract summary: We propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLMs framework for healthcare data management. 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.
• Score: 15.283746540652759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective [53.48484062444108]
  We find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs, on the other hand, MLLMs can facilitate the development of data. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective.
  arXiv  Detail & Related papers  (2024-07-11T15:08:11Z)
• MMRel: A Relation Understanding Dataset and Benchmark in the MLLM Era [72.95901753186227]
  Multi-Modal Relation Understanding (MMRel) is a comprehensive dataset for studying inter-object relations with Multi-modal Large Language Models (MLLMs) MMRel features three distinctive attributes: (i) It includes over 15K question-answer pairs, which are sourced from three distinct domains, ensuring large scale and high diversity; (ii) It contains a subset featuring highly unusual relations, on which MLLMs often fail due to hallucinations, thus are very challenging; (iii) It provides manually verified high-quality labels for inter-object relations.
  arXiv  Detail & Related papers  (2024-06-13T13:51:59Z)
• Developing Healthcare Language Model Embedding Spaces [0.20971479389679337]
  Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text. Three methods are assessed: traditional masked language modeling, Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR) and a novel pre-training objective utilizing metadata categories from the healthcare settings. Contrastively trained models outperform other approaches on the classification tasks, delivering strong performance from limited labeled data and with fewer model parameter updates required.
  arXiv  Detail & Related papers  (2024-03-28T19:31:32Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics [32.10937977924507]
  The utilization of large language models (LLMs) in the Healthcare domain has generated both excitement and concern. This survey outlines the capabilities of the currently developed LLMs for Healthcare and explicates their development process.
  arXiv  Detail & Related papers  (2023-10-09T13:15:23Z)
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
  Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
  arXiv  Detail & Related papers  (2023-10-06T10:40:46Z)
• MLLM-DataEngine: An Iterative Refinement Approach for MLLM [62.30753425449056]
  We propose a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop, the MLLM-DataEngine first analyze the weakness of the model based on the evaluation results. For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts the ratio of different types of data. For quality, we resort to GPT-4 to generate high-quality data with each given data type.
  arXiv  Detail & Related papers  (2023-08-25T01:41:04Z)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)
• Multimodal LLMs for health grounded in individual-specific data [1.8473477867376036]
  Foundation large language models (LLMs) have shown an impressive ability to solve tasks across a wide range of fields including health. We take a step towards creating multimodal LLMs for health that are grounded in individual-specific data. We show that HeLM can effectively use demographic and clinical features in addition to high-dimensional time-series data to estimate disease risk.
  arXiv  Detail & Related papers  (2023-07-18T07:12:46Z)
• Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources [40.32772510980854]
  "patchwork learning" (PL) is a paradigm that integrates information from disparate datasets composed of different data modalities. PL allows the simultaneous utilization of complementary data sources while preserving data privacy. We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources.
  arXiv  Detail & Related papers  (2023-05-10T14:50:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.