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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Integrated image management, artificial intelligence (AI), and reporting systems have revolutionized diagnostic pathology practice. In this paper, we introduce FlexLIS, a state-of-the-art system that enables AI to assist pathologists in performing histopathology image assessments and generating diagnostic reports.
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This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more...
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The micronucleus (MN) assay is a well-established test for quantifying DNA damage. However, scoring the assay using conventional techniques such as manual microscopy or feature-based image analysis is laborious and challenging. This paper describes the methodology to develop an artificial intelligence model to score the MN assay using imaging flow cytometry...
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A process of registering cone-beam computed tomography scans and digital dental images has been presented using artificial intelligence (AI) -assisted identification of landmarks and merging. A comparison with surface-based registration shows that AI-based digitization and integration are reliable and...
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相关实验视频

Updated: Jan 20, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

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将参考管理软件与基于人工智能的新工具进行比较.

Jae Gyeong Jin1, Seung Gyu Lee1, Jea Hyeun Park1

  • 1Research Factory and Publication Inc., Incheon, Korea.

Journal of educational evaluation for health professions
|January 18, 2026
PubMed
概括

参考管理软件 (RMS) 有助于学术写作,但当前的工具与人工智能生成的错误作斗争. 像CiteWell这样的新人工智能驱动的RMS为现代学术提供了增强的验证和可用性.

关键词:
人工智能幻觉 幻觉学术诚信 学术诚信引用 很好 引用 好这是PubMed验证的结果.参考管理软件是参考管理软件.

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相关实验视频

Last Updated: Jan 20, 2026

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科学领域:

  • 图书统计学 图书统计学
  • 学术传播学术交流
  • 信息科学 信息科学 信息科学

背景情况:

  • 参考管理软件 (RMS) 对于学术写作至关重要,有助于引用组织和文献编格.
  • 像EndNote,Zotero和Mendeley这样的现有平台在可用性,可访问性和准确性方面存在局限性.
  • 生成型人工智能引入了伪造引用的风险,这是传统RMS无法解决的挑战.

研究的目的:

  • 提供参考管理软件的历史概述.
  • 评估主要RMS平台的优缺点.
  • 为当代学术挑战引入新兴的基于AI的RMS解决方案.

主要方法:

  • 参考管理软件发展的文献综述.
  • 对已建立的RMS平台进行比较分析 (EndNote,Zotero,Mendeley).
  • 介绍和评估一个AI时代的RMS (CiteWell) 具有新的功能.

主要成果:

  • 传统的RMS平台缺乏验证引用真实性的能力,对人工智能生成的内容构成风险.
  • CiteWell提供AI时代的创新,包括PubMed集成验证,用户友好的界面,可定制的样式和多语言支持.
  • 新兴的基于人工智能的RMS代表了解决当前学术写作挑战的新范式.

结论:

  • 在人工智能时代,对可以验证引用真实性的RMS有着至关重要的需求.
  • 像CiteWell这样的人工智能驱动的RMS工具为研究人员提供了增强的保护和可用性.
  • 将AI整合到RMS中对于保持学术出版的准确性和完整性至关重要.