A Survey for Large Language Models in Biomedicine

• URL: http://arxiv.org/abs/2409.00133v1
• Date: Thu, 29 Aug 2024 12:39:16 GMT
• Title: A Survey for Large Language Models in Biomedicine
• Authors: Chong Wang, Mengyao Li, Junjun He, Zhongruo Wang, Erfan Darzi, Zan Chen, Jin Ye, Tianbin Li, Yanzhou Su, Jing Ke, Kaili Qu, Shuxin Li, Yi Yu, Pietro Liò, Tianyun Wang, Yu Guang Wang, Yiqing Shen,
• Abstract summary: This review is based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv. We explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine. We discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics.
• Score: 31.719451674137844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs.

Related papers

• BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature [73.39593644054865]
  BIOMEDICA is a scalable, open-source framework to extract, annotate, and serialize the entirety of the PubMed Central Open Access subset into an easy-to-use, publicly accessible dataset. Our framework produces a comprehensive archive with over 24 million unique image-text pairs from over 6 million articles. BMCA-CLIP is a suite of CLIP-style models continuously pretrained on the BIOMEDICA dataset via streaming, eliminating the need to download 27 TB of data locally.
  arXiv  Detail & Related papers  (2025-01-13T09:58:03Z)
• Large Language Models for Bioinformatics [58.892165394487414]
  This survey focuses on the evolution, classification, and distinguishing features of bioinformatics-specific language models (BioLMs) We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities.
  arXiv  Detail & Related papers  (2025-01-10T01:43:05Z)
• A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences [1.8308043661908204]
  This paper reviews the state-of-the-art applications of large language models (LLMs) in the biomedical domain. LLMs demonstrate remarkable potential, but significant challenges remain, including issues related to hallucinations, contextual understanding, and the ability to generalize. We aim to improve access to medical literature and facilitate meaningful discoveries in healthcare.
  arXiv  Detail & Related papers  (2024-12-04T18:26:13Z)
• A Survey of Medical Vision-and-Language Applications and Their Techniques [48.268198631277315]
  Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics.
  arXiv  Detail & Related papers  (2024-11-19T03:27:05Z)
• 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. 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)
• Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
  Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape. RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
  arXiv  Detail & Related papers  (2024-07-17T07:44:18Z)
• Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis [24.532570258954898]
  Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI) This study aims to provide a comprehensive overview of LLM applications in BHI, highlighting their transformative potential and addressing the associated ethical and practical challenges.
  arXiv  Detail & Related papers  (2024-03-24T21:29:39Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• 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)
• Interpretability from a new lens: Integrating Stratification and Domain knowledge for Biomedical Applications [0.0]
  This paper proposes a novel computational strategy for the stratification of biomedical problem datasets into k-fold cross-validation (CVs) This approach can improve model stability, establish trust, and provide explanations for outcomes generated by trained IML models.
  arXiv  Detail & Related papers  (2023-03-15T12:02:02Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

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.