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Issues And Trends In Healthcare Delivery System

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.
Cost Containment
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Automated Noninvasive Burn Diagnostic System for Health Care Using Artificial Intelligence: AMBUSH-AI.

Mohamed El Masry^1,2, Md Masudur Rahman^3,4, Surya C Gnyawali^1,2

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

Annals of Surgery

|February 19, 2026

Summary

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 diagnosis GPT TDI ultrasound burn depth prediction deep learning explainability surgical decision-making vision-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.