Merging Clinical Knowledge into Large Language Models for Medical Research and Applications: A Survey

• URL: http://arxiv.org/abs/2502.20988v1
• Date: Fri, 28 Feb 2025 12:00:51 GMT
• Title: Merging Clinical Knowledge into Large Language Models for Medical Research and Applications: A Survey
• Authors: Qiyuan Li, Haijiang Liu, Caicai Guo, Deyu Chen, Meng Wang, Feng Gao, Jinguang Gu,
• Abstract summary: Medical artificial intelligence (medical AI) aims to apply academic medical AI systems to real-world medical scenarios.<n>This survey focuses on the building paradigms of medical AI systems including the use of clinical databases, datasets, training pipelines, integrating medical knowledge graphs, system applications, and evaluation systems.
• Score: 9.359273536688066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical knowledge is the collection of information learned from studies on the causes, prognosis, diagnosis, and treatment of diseases. This type of knowledge can improve curing performances, and promote physical health. With the emergence of large language models (LLMs), medical artificial intelligence (medical AI), which aims to apply academic medical AI systems to real-world medical scenarios, has entered a new age of development, resulting in excellent works such as DoctorGPT and Pangu-Drug from academic and industrial researches. However, the field lacks a comprehensive compendium and comparison of building medical AI systems from academia and industry. Therefore, this survey focuses on the building paradigms of medical AI systems including the use of clinical databases, datasets, training pipelines, integrating medical knowledge graphs, system applications, and evaluation systems. We hope that this survey can help relevant practical researchers understand the current performance of academic models in various fields of healthcare, as well as the potential problems and future directions for implementing these scientific achievements.

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