The advent of precision medicine has revolutionized cancer treatment by integrating individual genetic, lifestyle, and environmental factors to tailor patient care (Huang et al., 2020; Ginsburg and Phillips, 2018). However, the complexity and heterogeneity of diseases like Multiple Myeloma (MM) pose significant challenges in leveraging the vast amounts of genomic data and biomedical literature available for personalized treatment planning (Rajkumar, 2014; Rollig et al., 2015). To address this, we present an innovative Retrieval-Augmented Generation (RAG) based chatbot framework that harnesses the power of Natural Language Processing (NLP) and state-of-the-art language models to curate and analyze MM-specific literature and provide personalized treatment recommendations based on patient-specific genomic data (Lewis et al., 2020). Our framework integrates the BioMed-RoBERTa-base model for embedding generation (Gururangan et al., 2020) and the Mistral-7B language model for question answering (Anthropic, 2023), enabling effective understanding and response to complex clinical queries. The retrieval component is enhanced by Amazon OpenSearch Service, ensuring fast and accurate access to relevant information. A comprehensive data analysis pipeline, including exploratory data analysis, semantic search, clustering, and topic modeling, provides valuable insights into the MM research landscape, informing the chatbot's knowledge base and uncovering potential research directions (Blei et al., 2003; Mikolov et al., 2013). Deployed using Amazon Kendra, our RAG chatbot offers a user-friendly and scalable platform for accessing MM information, incorporating features such as user authentication, customizable web interface, and continuous improvement based on user feedback. The framework aims to democratize access to precision medicine by providing clinicians with a sophisticated tool for interpreting complex genomic data in the context of MM, streamlining clinical workflows, and facilitating the development of personalized treatment plans (Patel et al., 2015). This paper presents the conceptualization, development, and potential impact of our RAG-based chatbot framework on the landscape of MM treatment and precision medicine. We argue that the synergistic integration of AI, NLP, and domain-specific knowledge marks a new era of healthcare, characterized by highly personalized, data-driven, and effective treatment modalities (Thong et al., 2021). Our framework not only advances the field of precision medicine in MM but also serves as a blueprint for the development of similar systems in other complex diseases, ultimately improving patient outcomes and quality of life.

中文翻译：

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用于多发性骨髓瘤精准医学的 RAG 聊天机器人

精准医疗的出现通过整合个体遗传、生活方式和环境因素来定制患者护理，彻底改变了癌症治疗（Huang et al., 2020；Ginsburg and Phillips, 2018）。然而，多发性骨髓瘤 (MM) 等疾病的复杂性和异质性对利用大量基因组数据和生物医学文献进行个性化治疗规划提出了重大挑战（Rajkumar，2014；Rollig 等，2015）。为了解决这个问题，我们提出了一种基于检索增强生成 (RAG) 的创新聊天机器人框架，该框架利用自然语言处理 (NLP) 和最先进的语言模型的力量来策划和分析 MM 特定文献并提供个性化的信息基于患者特定基因组数据的治疗建议（Lewis 等人，2020）。我们的框架集成了用于嵌入生成的 BioMed-RoBERTa-base 模型（Gururangan 等人，2020）和用于问答的 Mistral-7B 语言模型（Anthropic，2023），从而能够有效理解和响应复杂的临床查询。Amazon OpenSearch Service 增强了检索组件，确保快速、准确地访问相关信息。全面的数据分析管道，包括探索性数据分析、语义搜索、聚类和主题建模，为 MM 研究领域提供了宝贵的见解，为聊天机器人的知识库提供信息并揭示潜在的研究方向（Blei 等人，2003 年；Mikolov 等人） .，2013）。我们的 RAG 聊天机器人使用 Amazon Kendra 进行部署，提供了一个用户友好且可扩展的平台，用于访问 MM 信息，并结合了用户身份验证、可定制 Web 界面以及根据用户反馈进行持续改进等功能。该框架旨在通过为临床医生提供复杂的工具来解释 MM 背景下的复杂基因组数据、简化临床工作流程并促进个性化治疗计划的制定，从而实现精准医疗的民主化（Patel 等，2015）。本文介绍了我们基于 RAG 的聊天机器人框架的概念、开发以及对 MM 治疗和精准医疗领域的潜在影响。我们认为，人工智能、自然语言处理和特定领域知识的协同整合标志着医疗保健的新时代，其特点是高度个性化、数据驱动和有效的治疗方式（Thong et al., 2021）。我们的框架不仅推动了多发性骨髓瘤精准医学领域的发展，而且还为其他复杂疾病中类似系统的开发提供了蓝图，最终改善患者的治疗结果和生活质量。