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Related Concept Videos

Ethical Standards I01:25

Ethical Standards I

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The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
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Ethical Standards II01:23

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Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
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Integrated Healthcare System01:20

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Healthcare Agencies II01:17

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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.
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Related Experiment Video

Updated: Sep 8, 2025

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey.

Mansoor Ali, Faisal Naeem, Muhammad Tariq

    IEEE Journal of Biomedical and Health Informatics
    |June 13, 2022
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    Summary
    This summary is machine-generated.

    Federated learning (FL) addresses privacy concerns in smart healthcare by enabling artificial intelligence (AI) on Internet of Medical Things (IoMT) devices without sharing sensitive user data. This approach enhances security in connected health 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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    Area of Science:

    • Smart Healthcare Systems
    • Internet of Medical Things (IoMT)
    • Artificial Intelligence (AI) in Healthcare

    Background:

    • Advancements in electronic devices and communication infrastructure have led to the development of smart healthcare systems utilizing IoMT devices.
    • Centralized AI training in IoMT raises significant privacy issues due to the transmission of sensitive, confidential data between hospitals and end-users.
    • Existing systems are vulnerable to data exposure and breaches, necessitating robust privacy-preserving solutions.

    Purpose of the Study:

    • To identify and discuss privacy-related issues inherent in current IoMT ecosystems.
    • To explore the role and application of Federated Learning (FL) as a privacy-preserving paradigm in IoMT networks.
    • To introduce advanced FL architectures integrated with Deep Reinforcement Learning (DRL), Digital Twin, and Generative Adversarial Networks (GANs) for enhanced privacy threat detection.

    Main Methods:

    • Review and analysis of privacy challenges in IoMT systems.
    • Introduction of Federated Learning (FL) principles for decentralized AI training.
    • Integration of advanced AI techniques (DRL, Digital Twin, GANs) within FL frameworks for privacy threat detection in IoMT.

    Main Results:

    • Federated Learning (FL) offers a viable solution for privacy preservation in IoMT by training AI models locally, sharing only gradients, not raw data.
    • Advanced FL architectures demonstrate potential in actively detecting and mitigating privacy threats within smart healthcare networks.
    • Identified practical opportunities and applications for FL in enhancing the security and privacy of IoMT-based healthcare.

    Conclusions:

    • FL is a promising approach to safeguard sensitive health information in IoMT, addressing critical privacy concerns.
    • The integration of DRL, Digital Twin, and GANs with FL can significantly bolster privacy protection mechanisms.
    • Further research is needed to address open challenges and fully realize the potential of FL in future smart healthcare systems.