Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis
- URL: http://arxiv.org/abs/2403.16303v4
- Date: Sun, 28 Jul 2024 03:24:37 GMT
- Title: Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis
- Authors: Huizi Yu, Lizhou Fan, Lingyao Li, Jiayan Zhou, Zihui Ma, Lu Xian, Wenyue Hua, Sijia He, Mingyu Jin, Yongfeng Zhang, Ashvin Gandhi, Xin Ma,
- Abstract summary: 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.
- Score: 24.532570258954898
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research. 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. We reviewed 1,698 research articles from January 2022 to December 2023, categorizing them by research themes and diagnostic categories. Additionally, we conducted network analysis to map scholarly collaborations and research dynamics. Our findings reveal a substantial increase in the potential applications of LLMs to a variety of BHI tasks, including clinical decision support, patient interaction, and medical document analysis. Notably, LLMs are expected to be instrumental in enhancing the accuracy of diagnostic tools and patient care protocols. The network analysis highlights dense and dynamically evolving collaborations across institutions, underscoring the interdisciplinary nature of LLM research in BHI. A significant trend was the application of LLMs in managing specific disease categories such as mental health and neurological disorders, demonstrating their potential to influence personalized medicine and public health strategies. LLMs hold promising potential to further transform biomedical research and healthcare delivery. While promising, the ethical implications and challenges of model validation call for rigorous scrutiny to optimize their benefits in clinical settings. This survey serves as a resource for stakeholders in healthcare, including researchers, clinicians, and policymakers, to understand the current state and future potential of LLMs in BHI.
Related papers
- 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) - Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.
Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
arXiv Detail & Related papers (2024-10-06T18:46:28Z) - From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice [12.390859712280328]
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) - Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials [49.19897427783105]
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift.
We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes.
arXiv Detail & Related papers (2024-09-06T02:03:38Z) - A Survey for Large Language Models in Biomedicine [31.719451674137844]
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.
arXiv Detail & Related papers (2024-08-29T12:39:16Z) - 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) - A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions [31.04135502285516]
Large language models (LLMs) have received substantial attention due to their impressive capabilities for generating and understanding human-level language.
LLMs have emerged as an innovative and powerful adjunct in the medical field, transforming traditional practices and heralding a new era of enhanced healthcare services.
arXiv Detail & Related papers (2024-06-06T03:15:13Z) - Evaluating large language models in medical applications: a survey [1.5923327069574245]
Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains.
evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information.
arXiv Detail & Related papers (2024-05-13T05:08:33Z) - 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) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - 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)
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.