Jove
Visualize
联系我们
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

相关概念视频

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

966
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
966
SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

6.2K
SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
6.2K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.6K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.6K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

392
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
392

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

Mutation enrichment in targeted panels flags immunotherapy-responsive POLE-driven hypermutated microsatellite-stable colorectal cancers.

NPJ precision oncology·2026
Same author

Established machine learning matches tabular foundation models in clinical predictions.

BMC medical informatics and decision making·2026
Same author

A deep learning framework for efficient pathology image analysis.

Nature communications·2026
Same author

Erratum for: Associations of MRI-derived Paraspinal IMAT and LMM with Cardiometabolic Risk Factors: Results from a German Cohort.

Radiology·2026
Same author

clickBrick prompt engineering: optimizing large language model performance in clinical psychiatry.

Npj mental health research·2026
Same author

Gut decisions based on the liver: prediction of colorectal neoplasia using AI-based liver analysis of routine CT scans.

Frontiers in oncology·2026
Same journal

Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering.

NPJ digital medicine·2026
Same journal

Fast information and slow evidence in the large language models era.

NPJ digital medicine·2026
Same journal

Predicting response to neoadjuvant therapy using artificial intelligence on digitized histopathology slides: a systematic review.

NPJ digital medicine·2026
Same journal

Automated diagnosis of keratitis from low-quality slit-lamp images using an improved generative adversarial network.

NPJ digital medicine·2026
Same journal

The ethics of listening walls: patient autonomy and consent in the age of ambient clinical AI.

NPJ digital medicine·2026
Same journal

Targeting m6A-SCG2-TAMs axis overcomes 5-FU resistance in colorectal cancer via a multi-omics model.

NPJ digital medicine·2026
查看所有相关文章

相关实验视频

Updated: Feb 20, 2026

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

1.2K

对临床决策任务的基于大型语言模型的代理系统进行基准测试.

Yunsong Liu1,2, Zunamys I Carrero2, Xiaofeng Jiang2,3

  • 1Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

NPJ digital medicine
|February 18, 2026
PubMed
概括
此摘要是机器生成的。

代理人工智能 (AI) 系统在医疗保健中表现出有限的性能增长,尽管有先进的工具. 目前的系统在高计算成本下提供了适度的好处,突出了需要改进的人工智能解决方案的需求.

更多相关视频

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

1.6K

相关实验视频

Last Updated: Feb 20, 2026

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

1.2K
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

1.6K

科学领域:

  • 人工智能的人工智能
  • 医疗信息学 医疗信息学
  • 计算医学是一种计算医学.

背景情况:

  • 能够自主推理和使用工具的代理人工智能系统在医疗保健应用中显示出潜力.
  • 目前,对这些先进的AI系统在医学中的系统化现实世界性能评估是有限的.
  • 现有的基准没有完全捕捉到临床决策和工具集成的复杂性.

研究的目的:

  • 在医疗保健环境中系统地比较两个代理人工智能系统的现实世界性能.
  • 评估代理AI在各种医疗任务中的有效性,包括诊断,质量保证和复杂的检查.
  • 评估医疗人工智能剂中性能提升,资源利用率和幻觉率之间的权衡.

主要方法:

  • 在AgentClinic,MedAgentsBench和人类最后一次考试 (HLE) 的基准上评估了OpenManus (基于Llama-4) 和Manus (专有多步架构).
  • 评估了基于文本和多模式的医疗问答和诊断模拟的性能.
  • 量化准确性,代币使用,延迟和幻觉率,具有内部安全措施.

主要成果:

  • 代理人工智能系统比基线LLM提供了适度的准确性改进,令牌使用和延迟显著增加.
  • 在AgentClinic MedQA上的准确率达到60.3%,MedAgentsBench 30.3%,HLE文本的准确率达到8.6%.
  • 多模式准确性很低 (15.5%在HLE上,29.2%在AgentClinic NEJM上),尽管采取了安全措施,幻觉仍然存在.

结论:

  • 目前的代理人工智能设计相对于其大量的计算和工作流程成本,在医疗保健中提供了有限的性能优势.
  • 发展更准确,更有效,更具临床可行性的药剂系统对于医疗应用来说是非常必要的.
  • 需要进一步的研究来优化对实际医疗保健部署的代理人工智能架构.