Advances in Large Language Models for Medicine

• URL: http://arxiv.org/abs/2509.18690v1
• Date: Tue, 23 Sep 2025 06:16:39 GMT
• Title: Advances in Large Language Models for Medicine
• Authors: Zhiyu Kan, Wensheng Gan, Zhenlian Qi, Philip S. Yu,
• Abstract summary: This paper systematically reviews the up-to-date research progress of large language models (LLMs) in the medical field.<n>It provides an in-depth analysis of training techniques for large medical models, their adaptation in healthcare settings, related applications, as well as their strengths and limitations.
• Score: 45.89197361522528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) technology has advanced rapidly in recent years, with large language models (LLMs) emerging as a significant breakthrough. LLMs are increasingly making an impact across various industries, with the medical field standing out as the most prominent application area. This paper systematically reviews the up-to-date research progress of LLMs in the medical field, providing an in-depth analysis of training techniques for large medical models, their adaptation in healthcare settings, related applications, as well as their strengths and limitations. Furthermore, it innovatively categorizes medical LLMs into three distinct types based on their training methodologies and classifies their evaluation approaches into two categories. Finally, the study proposes solutions to existing challenges and outlines future research directions based on identified issues in the field of medical LLMs. By systematically reviewing previous and advanced research findings, we aim to highlight the necessity of developing medical LLMs, provide a deeper understanding of their current state of development, and offer clear guidance for subsequent research.

Related papers

• 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)
• Demystifying Large Language Models for Medicine: A Primer [50.83806796466396]
  Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare. This tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice.
  arXiv  Detail & Related papers  (2024-10-24T15:41:56Z)
• From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice [14.739357670600103]
  Large Language Models (LLMs) have rapidly evolved from text-based systems to multimodal platforms.<n>We examine the current landscape of MLLMs in healthcare, analyzing their applications across clinical decision support, medical imaging, patient engagement, and research.
  arXiv  Detail & Related papers  (2024-09-14T02:35:29Z)
• Large Language Models for Medicine: A Survey [31.720633684205424]
  Large language models (LLMs) have been developed to address challenges in the digital economy's landscape of digital intelligence. This paper focuses on the requirements and applications of medical LLMs.
  arXiv  Detail & Related papers  (2024-05-20T02:32:26Z)
• A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine [9.116392782378753]
  Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal large language models (MLLMs) have attracted widespread attention.<n>This survey provides a comprehensive overview of the development, principles, application scenarios, challenges, and future directions of LLMs and MLLMs in medicine.
  arXiv  Detail & Related papers  (2024-05-14T13:42:05Z)
• Large Language Model Distilling Medication Recommendation Model [58.94186280631342]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)<n>Our research aims to transform existing medication recommendation methodologies using LLMs.<n>To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Large language models in healthcare and medical domain: A review [4.456243157307507]
  Large language models (LLMs) provide proficient responses to free-text queries. This review explores the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications.
  arXiv  Detail & Related papers  (2023-12-12T20:54:51Z)
• A Survey of Large Language Models in Medicine: Progress, Application, and Challenge [85.09998659355038]
  Large language models (LLMs) have received substantial attention due to their capabilities for understanding and generating human language. This review aims to provide a detailed overview of the development and deployment of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-09T02:55:58Z)
• Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review [16.008511195589925]
  Large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This paper provides a comprehensive review on the applications and implications of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-03T13:51:36Z)
• A Comprehensive Overview of Large Language Models [68.22178313875618]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. This article provides an overview of the existing literature on a broad range of LLM-related concepts.
  arXiv  Detail & Related papers  (2023-07-12T20:01:52Z)

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.