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Issues And Trends In Healthcare Delivery System

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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Related Experiment Video

Updated: Jun 4, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging.

Pranav Kulkarni1, Adway Kanhere1, Eliot L Siegel1

  • 1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, 100 N Greene St, Baltimore, MD, 21201, USA.

Journal of Imaging Informatics in Medicine
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

Intelligent streaming framework ISLE reduces bandwidth and computational needs for AI in radiology. This optimizes AI inference, significantly boosting processing speed without affecting diagnostic accuracy.

Keywords:
CompressionDeep learningImage streamingMedical imagingProgressive encoding

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Health Informatics

Background:

  • Growing adoption of AI in radiology increases demand for bandwidth and computational resources.
  • High infrastructural costs are a challenge for healthcare providers and AI vendors.
  • Current imaging infrastructures face inefficiencies in supporting AI deployments.

Purpose of the Study:

  • To develop an intelligent streaming framework, ISLE, to address inefficiencies in medical imaging infrastructures.
  • To reduce bandwidth and computational requirements for AI inference in radiology.
  • To increase the throughput of AI systems processing medical images.

Main Methods:

  • Developed ISLE, an intelligent streaming framework inspired by video-on-demand platforms.
  • Intelligently streams medical images at optimal resolution for AI inference using progressive encoding.
  • Evaluated ISLE by streaming chest X-rays for classification and abdomen CT scans for segmentation.

Main Results:

  • ISLE reduced data transmission and decoding time by at least 82% for all tasks.
  • Throughput increased by over 2.9× for segmentation and 3.72× for classification.
  • AI diagnostic performance remained unaffected (P > 0.05) across all evaluated tasks.

Conclusions:

  • ISLE effectively addresses imaging infrastructure inefficiencies for AI deployments.
  • The framework improves data and computational efficiency in clinical AI applications.
  • ISLE enables efficient AI deployment without compromising clinical decision-making.