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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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AI-Driven Integrated System for Burn Depth Prediction With Electronic Medical Records: Algorithm Development and

Md Masudur Rahman1, Mohamed El Masry2,3, Surya C Gnyawali2,3

  • 1Edwardson School of Industrial Engineering, Purdue University West Lafayette, 315 N Grant Street, West Lafayette, IN, 47907, United States, 1 765 496 7380.

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Summary
This summary is machine-generated.

A new artificial intelligence (AI) system accurately classifies burn depth using integrated digital photos and ultrasound imaging. This AI tool, integrated into electronic medical records (EMR), improves diagnostic accuracy for burn injuries.

Keywords:
EMRburn diagnosiselectronic medical recordlarge language modelultrasoundvision-language model

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Burn Management

Background:

  • Accurate burn depth assessment is crucial for effective treatment and patient outcomes.
  • Traditional visual inspection methods have variable diagnostic accuracy, leading to suboptimal clinical decisions.
  • A need exists for a more consistent and precise approach to burn classification.

Purpose of the Study:

  • To evaluate a multimodal artificial intelligence (AI) system for accurate burn depth classification.
  • To determine if the AI system can preserve diagnostic accuracy within an electronic medical record (EMR).
  • To assess the AI system's utility as a clinical decision support tool.

Main Methods:

  • A multimodal AI system integrating digital photographs and ultrasound tissue Doppler imaging (TDI) was developed.
  • Imaging data was accessed and processed through an EMR system (DrChrono).
  • A GPT-4 based vision-language model interpreted images using expert-formulated prompts for classification.

Main Results:

  • The AI classifier achieved an overall accuracy of 84.38% in identifying burn degrees.
  • Performance significantly surpassed typical human diagnostic accuracy benchmarks.
  • Area under the receiver operating characteristic curves demonstrated high performance: 0.97 (1st-degree), 0.96 (2nd-degree), and 1.00 (3rd-degree).

Conclusions:

  • Multimodal AI analysis of EMR-stored imaging data enables real-time burn depth prediction.
  • The system enhances diagnostic efficiency by utilizing digital photos for superficial burns and TDI for deeper burns.
  • This AI approach offers significant advancements for burn care, particularly in resource-limited settings.