Jove
Visualize
联系我们

相关概念视频

Microorganisms in Medicine and Therapeutics01:29

Microorganisms in Medicine and Therapeutics

Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Advances in Wearable Biosensors for Non-Invasive Biofluid Monitoring.

Biosensors·2026
Same author

Design and validation of a technology for 3D printing training phantoms for ultrasound imaging.

Physical and engineering sciences in medicine·2025
Same author

Large Language Models in Healthcare and Medical Applications: A Review.

Bioengineering (Basel, Switzerland)·2025
Same author

IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM.

PeerJ. Computer science·2025
Same author

Automated Detection of Aberrant Episodes in Epileptic Conditions: Leveraging EEG and Machine Learning Algorithms.

Bioengineering (Basel, Switzerland)·2025
Same author

Large scale summarization using ensemble prompts and in context learning approaches.

Scientific reports·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jul 3, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.9K

医学LLM:微调与检索增强生成

Bhagyajit Pingua1,2, Adyakanta Sahoo1,3, Meenakshi Kandpal2

  • 1Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA.

Bioengineering (Basel, Switzerland)
|July 29, 2025
PubMed
概括

检索增强生成 (RAG) 和联合微调 (FT+RAG) 提高了大型语言模型 (LLM) 在医疗保健中的性能,远远超过仅微调. 通过这些专门的培训方法,LLAMA和PHI模型显示出最好的结果.

关键词:
精细调整 精细调整 精细调整医疗保健 医疗保健 医疗保健 医疗保健大型语言模型.医疗 医疗 医疗 医疗提取增强生成的提取

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

684
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

584

相关实验视频

Last Updated: Jul 3, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.9K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

684
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

584

科学领域:

  • 人工智能的人工智能
  • 医疗信息学 医疗信息学
  • 自然语言处理自然语言处理.

背景情况:

  • 大型语言模型 (LLM) 拥有广泛的知识,但缺乏专门的专业知识.
  • 通过有针对性的培训方法,可以提高特定领域的LLM绩效.
  • 医疗保健应用需要专门的LLM,能够准确可靠地检索和生成信息.

研究的目的:

  • 评估检索增强生成 (RAG) 和微调 (FT) 对五种不同的大型语言模型的医疗保健数据的有效性.
  • 为了比较单独使用RAG,单独使用FT和结合使用FT+RAG的方法的性能.
  • 确定哪些模型和培训策略为专门的医疗保健应用提供了优异的结果.

主要方法:

  • 五个大型语言模型 (LLama-3.1-8B,Gemma-2-9B,Mistral-7B-Instruct,Qwen2.5-7B,Phi-3.5-Mini-Instruct) 在MedQuAD数据集上进行了微调.
  • 模型使用三个方法进行训练:仅检索增强生成 (RAG),仅微调 (FT),以及两者的组合 (FT+RAG).
  • 在多个指标上评估性能,以评估特定领域的准确性和能力.

主要成果:

  • 在大多数评估模型中,检索增强生成 (RAG) 和组合微调 (FT+RAG) 始终优于单独微调 (FT).
  • LLAMA和PHI模型表现出卓越的性能,LLAMA显示出整体卓越性,PHI在RAG/FT+RAG能力方面表现出色.
  • QWEN模型在性能方面普遍落后,而GEMMA和MISTRAL则根据培训方法表现出不同的结果.

结论:

  • 专门的培训方法,特别是RAG和FT+RAG,对于提高LLM在医疗保健等利基领域的绩效至关重要.
  • 选择LLM架构显著影响不同培训策略的有效性.
  • 对优化这些培训方法的进一步研究可以为AI在医疗信息系统中发挥更大的潜力.