关键词: 体系化人工智能, 大语言模型, 知识分解, 原生网络

Abstract: Large language models ( LLMs ) are recognized for their powerful self-learning and understanding skills, as well as their huge development potential and application value in medical domain. However, current LLMs in the medical field are marked by an urgent demand for a large amount of pre-training data, high computing power costs, and a lack of standardized standards and indicator systems, which greatly limits its expansion and application. To address the above-mentioned issues, a systematic large-scale modeling framework is proposed for the whole medical process service scenario. Knowledge factorization and dynamic resource management methods are utilized in this framework to achieve model simplification and native network construction, enabling elastic deployment and flexible configuration of the models. The reliance on computational resources is reduced to some extent by this approach. Furthermore, blockchain technology is incorporated into the framework to ensure the security and trustworthiness of medical data. By introducing the concept of holistic artificial intelligence, a holistic artificial intelligence system framework for the medical field is constructed, aiming to promote the rapid implementation and sustain healthy development of medical LLMs.

Key words: holistic artificial intelligence , large language models ,  knowledge factorization ,  nativenetwork