Privacy-Preserving Retrieval-Augmented Generation on Local Devices for Regenerative Medicine Applications

Abstract Retrieval-augmented generation (RAG) has emerged as a promising approach to improve the factual consistency and domain-specific accuracy of large language models (LLMs), particularly in fields that demand precise and up-to-date knowledge. However, existing RAG implementations are often cloud-based and unsuitable for sensitive domains such as clinical research and regenerative medicine, where data confidentiality is paramount. In this study, we propose a privacy-preserving RAG framework using Gemma 3, a lightweight local LLM, implemented and evaluated on a commercially available MacBook Air M3. The framework operates offline without external network access, ensuring robust data security, and is feasible even in institutions without high-performance computing infrastructure. We constructed a proprietary knowledge base centered on human embryonic stem cell (ES cell)-derived hepatocyte-like cells (HAES), integrating published literature, internal consultation records, and regulatory documents. The system demonstrates context-aware generation capabilities suitable for supporting technical inquiries related to HAES applications, such as differentiation markers, safety profiles, and clinical research protocols. While the local LLM inevitably shows some limitations compared to cloud-based large models in terms of general linguistic performance, the integration of a domain-specific retrieval system substantially compensates for this gap. This work highlights the feasibility of local-device RAG frameworks in advancing sensitive biomedical applications, offering a scalable, privacy-preserving, and clinically deployable alternative to cloud-based solutions. Competing Interest Statement AU is a stockholder of iHaes. The other authors declare no conflict of interest regarding the work described herein. Funding Statement This research was supported by the Grant of National Center for Child Health and Development (2021C-21). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The datasets and cells used during the current study are available from the corresponding author upon reasonable request.