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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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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Automated Non-Invasive Burn Diagnostic System for Healthcare using Artificial Intelligence: AMBUSH-AI.

Mohamed El Masry1,2, Md Masudur Rahman3,4, Surya C Gnyawali1,2

  • 1Department of Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15219.

Annals of Surgery
|February 19, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) combined with ultrasound imaging accurately predicts burn depth. This new technology shows high accuracy in identifying third-degree burns, improving diagnostic capabilities for burn wound care.

Keywords:
AI burn diagnosisGPTTDI ultrasoundburn depth predictiondeep learningexplainabilitysurgical decision-makingvision-language model

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Area of Science:

  • Medical Technology
  • Artificial Intelligence
  • Ultrasound Imaging

Background:

  • Accurate burn depth assessment is crucial for surgical decisions.
  • Current diagnostic accuracy for distinguishing deep partial-thickness from third-degree burns is limited (76% for experts, 50% for non-experts).
  • This limitation highlights the need for improved diagnostic tools in burn management.

Purpose of the Study:

  • To develop and evaluate an AI-driven technology for predicting burn wound depth.
  • To integrate FDA-approved ultrasound modalities with AI for enhanced diagnostic accuracy.
  • To overcome the diagnostic challenges in differentiating burn wound depths.

Main Methods:

  • An AI framework was developed using a pig burn model and tested in human subjects.
  • Tissue Doppler Elastography Imaging (TDI) and Harmonic B-mode ultrasound images were acquired.
  • AI interpreted TDI and B-mode images, with biopsies used as ground truth in some cases.

Main Results:

  • The AI algorithm achieved 100% accuracy in identifying third-degree burns in pigs.
  • In human subjects, the AI method demonstrated 95% accuracy in identifying third-degree burns.
  • The AI model analyzed TDI and B-mode ultrasound images to predict burn depth.

Conclusions:

  • AI interpretation of B-mode ultrasound and TDI images is a feasible strategy.
  • This approach significantly increases diagnostic accuracy in predicting burn depth.
  • The developed technology holds promise for improving burn wound assessment and treatment planning.