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Related Concept Videos

Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
The managed care system is designed to control the cost while maintaining the quality of care. The patient's care from admission to discharge is planned by the primary care provider or the case manager, also known as the gatekeeper. In a managed care system, the number of care providers is limited...
Healthcare Agencies II01:17

Healthcare Agencies II

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, and lay...
Healthcare Agencies I01:18

Healthcare Agencies I

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...
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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 illness...
Integrated Healthcare System01:20

Integrated Healthcare System

An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...

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Related Experiment Videos

Multimodal Federated Learning in Healthcare: A Review.

Jacob Thrasher1, Alina Devkota1, Prasiddha Siwakoti1

  • 1Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV USA.

Journal of Healthcare Informatics Research
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

Federated Learning (FL) enhances multimodal AI in healthcare by keeping patient data decentralized and secure. This approach advances AI applications while prioritizing patient privacy in medical data analysis.

Keywords:
Data securityDeep learningFederated learningHealthcareMultimodal learning

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Healthcare Informatics

Background:

  • Multimodal machine learning advances AI in medicine, often using centralized data.
  • Federated Learning (FL) offers a decentralized alternative, enhancing data privacy and security.
  • Integrating FL with multimodal learning addresses healthcare data privacy concerns.

Purpose of the Study:

  • To provide an overview of Federated Learning's significance in healthcare.
  • To outline current state-of-the-art Multimodal Federated Learning (MMFL) approaches in medicine.
  • To examine challenges and future directions in MMFL for healthcare.

Main Methods:

  • Literature review of current Multimodal Federated Learning (MMFL) techniques.
  • Analysis of existing challenges and limitations in MMFL models for healthcare.
  • Exploration of future research avenues for MMFL in the medical domain.

Main Results:

  • MMFL leverages decentralized data for secure, privacy-preserving AI in healthcare.
  • Current MMFL models face challenges in implementation and scalability.
  • Significant potential exists for advancing MMFL to bridge AI capabilities with data privacy.

Conclusions:

  • Multimodal Federated Learning is crucial for secure and private AI in healthcare.
  • Addressing current challenges is key to unlocking the full potential of MMFL.
  • Future research should focus on robust MMFL frameworks for widespread healthcare adoption.