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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Current Trends in Nursing II01:30

Current Trends in Nursing II

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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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Healthcare Agencies I01:18

Healthcare Agencies I

1.2K
Healthcare agencies provide healthcare services to people. In the United States, voluntary agencies are often non-profit centers sponsored by donations, grants, or fundraisers. One such organization is Meals on Wheels, which provides meals to the elderly and homebound. The American Heart Association and the American Lung Association are other non-profit community organizations. Doctors and nurses are frequently active members of these organizations, which offer health checks and educational...
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Healthcare Agencies II01:17

Healthcare Agencies II

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There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources,...
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関連する実験動画

Updated: Feb 24, 2026

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

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医療における説明可能なAI

Yupei Li1, Qiyang Sun1, Alican Akman1

  • 1GLAM - Group on Language, Audio, & Music, Imperial College London, UK.

Studies in health technology and informatics
|February 23, 2026
PubMed
まとめ

説明可能なAI(XAI)は、信頼できるヘルスケアアプリケーションにとって不可欠です。XAIは人工知能(AI)モデルの透明性を高め、臨床的意思決定と患者の信頼を向上させます。

科学分野:

  • コンピュータサイエンス
  • 人工知能
  • ヘルスケアテクノロジー

背景:

  • 医療における人工知能(AI)の統合は急速に進歩しており、診断、治療、患者ケアに影響を与えています。
  • 複雑なAIモデルはしばしば透明性を欠き、重要な医療上の意思決定においてリスクをもたらします。
  • 説明可能性は、情報に基づいた臨床的意思決定とAIに対する患者の信頼にとって不可欠です。

研究 の 目的:

  • 説明可能なAI(XAI)の基本概念を紹介します。
  • 医療におけるAIモデルの解釈可能性を高めるためのXAI方法論を分析します。
  • 多様な医療データモダリティにわたるXAIアプリケーションについて議論します。

主な方法:

  • 説明可能なAI(XAI)の基本概念のレビュー。
  • AIモデルの解釈可能性を高めるための著名な方法論の分析。
  • 時系列、テキスト、画像、音声の医療データにわたるXAIアプリケーションの調査。

主要な成果:

  • XAIは、医療従事者や患者にとって不可欠な透明性を提供します。
  • XAIは、さまざまな種類の医療データにわたる独自の課題に対処します。
キーワード:
AIアプリケーション人工知能デジタルヘルス説明可能なAIヘルスケア

関連する実験動画

Last Updated: Feb 24, 2026

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

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  • 医療分野に適用可能な著名なXAI方法論を特定しました。
  • 結論:

    • XAIは、医療におけるAIの安全かつ効果的な実装に不可欠です。
    • 医療におけるXAI実装の現在の制限と課題について議論しました。
    • 医療におけるAIの信頼性を高めるための有望な将来の研究の方向性を特定しました。