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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.
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  1. Home
  3. Towards Blockchain Based Federated Learning In Categorizing Healthcare Monitoring Devices On Artificial Intelligence Of Medical Things Investigative Framework.
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  3. Towards Blockchain Based Federated Learning In Categorizing Healthcare Monitoring Devices On Artificial Intelligence Of Medical Things Investigative Framework.

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Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence

Syed Thouheed Ahmed1, T R Mahesh2, E Srividhya3

  • 1Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, 502285, India.

BMC Medical Imaging
|May 10, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Artificial intelligence of medical thingsDevice categorizationDevice labelingFederated learningHealthcare systems

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This study introduces a novel method for categorizing Artificial Intelligence of Medical Things (AIoMT) devices using federated learning (FL). This approach enhances data privacy and enables efficient indexing of medical devices.

Area of Science:

  • Computer Science
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Categorizing Artificial Intelligence of Medical Things (AIoMT) devices is challenging within standard Internet of Things (IoT) and Internet of Medical Things (IoMT) frameworks.
  • Existing methods struggle with server and computational layer classification of AIoMT devices.

Purpose of the Study:

  • To present a novel methodology for categorizing AIoMT devices.
  • To leverage federated learning (FL) for decentralized processing and attribute extraction.
  • To ensure data privacy and operational policy adherence in AIoMT device classification.

Main Methods:

  • Deployment of a system on standard IoT and labeled IoMT devices for training and attribute extraction.
  • Utilizing a global federated aggregation server to map interconnected attributes.
  • Establishing a centralized knowledge repository for indexing and synchronization.

    • Main Results:

    • Successful extraction and mapping of interdependent device attributes via federated learning.
    • Development of a reliable categorization index for AIoMT devices.
    • Demonstrated effective classification and labeling across diverse IoT, IoMT, and AIoMT devices.

    Conclusions:

    • The proposed federated learning methodology provides an effective solution for AIoMT device categorization.
    • This approach facilitates efficient access and optimization of medical devices within global server infrastructures.
    • The technique ensures data privacy while enabling robust device classification.