Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond

• URL: http://arxiv.org/abs/2502.20988v2
• Date: Mon, 11 Aug 2025 15:03:23 GMT
• Title: Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond
• Authors: Qiyuan Li, Haijiang Liu, Caicai Guo, Chao Gao, Deyu Chen, Meng Wang, Feng Gao, Frank van Harmelen, Jinguang Gu,
• Abstract summary: Clinical knowledge has been extensively examined within real-world medical practices.<n>There has been a notable increase in research efforts aimed at integrating this type of knowledge into large language models.<n>We review the various initiatives to embed clinical knowledge into training-based, KG-supported, and RAG-assisted LLMs.
• Score: 17.18909853414425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The large-scale development of large language models (LLMs) in medical contexts, such as diagnostic assistance and treatment recommendations, necessitates that these models possess accurate medical knowledge and deliver traceable decision-making processes. Clinical knowledge, encompassing the insights gained from research on the causes, prognosis, diagnosis, and treatment of diseases, has been extensively examined within real-world medical practices. Recently, there has been a notable increase in research efforts aimed at integrating this type of knowledge into LLMs, encompassing not only traditional text and multimodal data integration but also technologies such as knowledge graphs (KGs) and retrieval-augmented generation (RAG). In this paper, we review the various initiatives to embed clinical knowledge into training-based, KG-supported, and RAG-assisted LLMs. We begin by gathering reliable knowledge sources from the medical domain, including databases and datasets. Next, we evaluate implementations for integrating clinical knowledge through specialized datasets and collaborations with external knowledge sources such as KGs and relevant documentation. Furthermore, we discuss the applications of the developed medical LLMs in the industrial sector to assess the disparity between models developed in academic settings and those in industry. We conclude the survey by presenting evaluation systems applicable to relevant tasks and identifying potential challenges facing this field. In this review, we do not aim for completeness, since any ostensibly complete review would soon be outdated. Our goal is to illustrate diversity by selecting representative and accessible items from current research and industry practices, reflecting real-world situations rather than claiming completeness. Thus, we emphasize showcasing diverse approaches.

Related papers

• RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis [56.373297358647655]
  Retrieval-Augmented Diagnosis (RAD) is a novel framework that injects external knowledge into multimodal models directly on downstream tasks.<n>RAD operates through three key mechanisms: retrieval and refinement of disease-centered knowledge from multiple medical sources, a guideline-enhanced contrastive loss transformer, and a dual decoder.
  arXiv  Detail & Related papers  (2025-09-24T10:36:14Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Can LLMs Support Medical Knowledge Imputation? An Evaluation-Based Perspective [1.4913052010438639]
  We have explored the use of Large Language Models (LLMs) for imputing missing treatment relationships.<n>LLMs offer promising capabilities in knowledge augmentation, but their application in medical knowledge imputation presents significant risks.<n>Our findings highlight critical limitations, including inconsistencies with established clinical guidelines and potential risks to patient safety.
  arXiv  Detail & Related papers  (2025-03-29T02:52:17Z)
• Biomedical Foundation Model: A Survey [84.26268124754792]
  Foundation models are large-scale pre-trained models that learn from extensive unlabeled datasets. These models can be adapted to various applications such as question answering and visual understanding. This survey explores the potential of foundation models across diverse domains within biomedical fields.
  arXiv  Detail & Related papers  (2025-03-03T22:42:00Z)
• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
  This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods. By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
  arXiv  Detail & Related papers  (2024-07-07T18:02:00Z)
• M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering [14.198330378235632]
  We use Multiple Choice and Abstractive Question Answering to conduct a large-scale empirical study on 22 datasets in three generalist and three specialist biomedical sub-domains. Our multifaceted analysis of the performance of 15 LLMs uncovers success factors such as instruction tuning that lead to improved recall and comprehension. We show that while recently proposed domain-adapted models may lack adequate knowledge, directly fine-tuning on our collected medical knowledge datasets shows encouraging results. We complement the quantitative results with a skill-oriented manual error analysis, which reveals a significant gap between the models' capabilities to simply recall necessary knowledge and to integrate it with the presented
  arXiv  Detail & Related papers  (2024-06-06T02:43:21Z)
• A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry [2.1717945745027425]
  Large Language Models (LLMs) have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation. This comprehensive survey delineates the extensive application and requisite evaluation of LLMs within healthcare. Our survey is structured to provide an in-depth analysis of LLM applications across clinical settings, medical text data processing, research, education, and public health awareness.
  arXiv  Detail & Related papers  (2024-04-24T09:55:24Z)
• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models [56.36916128631784]
  We introduce MedBench, a comprehensive benchmark for the Chinese medical domain. This benchmark is composed of four key components: the Chinese Medical Licensing Examination, the Resident Standardization Training Examination, and real-world clinic cases. We perform extensive experiments and conduct an in-depth analysis from diverse perspectives, which culminate in the following findings.
  arXiv  Detail & Related papers  (2023-12-20T07:01:49Z)
• A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [59.4999994297993]
  This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs)<n>We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications.<n>The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice.
  arXiv  Detail & Related papers  (2023-06-07T21:51:56Z)
• Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey [0.0]
  Artificail Intelligence based framework is rapidly revolutionizing the healthcare industry. These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases. This paper focuses on the survey of machine learning and deep learning applications in across 16 medical specialties.
  arXiv  Detail & Related papers  (2021-07-19T14:26:03Z)
• Machine Learning Applications for Therapeutic Tasks with Genomics Data [49.98249191161107]
  We review the literature on machine learning applications for genomics through the lens of therapeutic development. We identify twenty-two machine learning in genomics applications across the entire therapeutics pipeline. We pinpoint seven important challenges in this field with opportunities for expansion and impact.
  arXiv  Detail & Related papers  (2021-05-03T21:20:20Z)
• Surgical Data Science -- from Concepts toward Clinical Translation [67.543698133416]
  Surgical Data Science aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. We shed light on the underlying reasons and provide a roadmap for future advances in the field.
  arXiv  Detail & Related papers  (2020-10-30T14:20:16Z)
• Artificial intelligence in medicine and healthcare: a review and classification of current and near-future applications and their ethical and social Impact [0.0]
  This work is based on an analysis of the state of the art of research and technology, including existing software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. Motivated by our review, we present and describe the notion of 'extended personalized medicine' We study the transformations of the roles of doctors and patients in an age of ubiquitous information, identify the risk of a division of Medicine into 'fake-based', 'patient-generated', and'scientifically tailored', and draw the attention of some aspects that need further thorough analysis
  arXiv  Detail & Related papers  (2020-01-22T15:39:42Z)
• Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems [18.95904791202457]
  We hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adrial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training.
  arXiv  Detail & Related papers  (2020-01-12T12:45:46Z)

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.