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Related Concept Videos

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Related Experiment Video

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.

Muhammad Tayyab Zamir1, Safir Ullah Khan2, Alexander Gelbukh1

  • 1Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Ciudad de México, CDMX, Mexico.

JMIR Formative Research
|September 25, 2025
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Summary
This summary is machine-generated.

Explainable AI (XAI) enhances trust in AI diagnostics by interpreting radiology reports. This study shows XAI improves health professionals' confidence and comprehension of AI-assisted medical decisions.

Keywords:
LIMELocal Interpretable Model-Agnostic ExplanationsSHAPShapley Adaptive Explanationsartificial intelligenceexplainable AInatural language processingradiology

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Natural Language Processing in Healthcare

Background:

  • Transparency in artificial intelligence (AI) is crucial for adoption in clinical diagnostics.
  • Explainable AI (XAI) offers a solution to improve interpretability and reliability of AI-driven medical decisions.

Purpose of the Study:

  • To evaluate XAI's effectiveness in interpreting radiology reports.
  • To enhance healthcare practitioners' confidence and understanding of AI-assisted diagnostic tools.

Main Methods:

  • Utilized the Indiana University chest x-ray dataset (3169 reports, 6471 images).
  • Employed various machine learning models for text classification (LSTM, GPT-2, T5, LLaMA-2, LLaMA-3.1) and image classification (DenseNet121, DenseNet169).
  • Applied XAI techniques, including SHAP and LIME, to interpret top-performing models.

Main Results:

  • LLaMA-3.1 achieved 98% accuracy in textual report classification (Cohen κ=0.981).
  • DenseNet121 and DenseNet169 models reached 84% accuracy in image analysis.
  • XAI methods identified key medical terms like 'opacity' and 'consolidation' as indicators of abnormal findings.

Conclusions:

  • Explainability is essential for AI diagnostic systems in healthcare.
  • XAI improves diagnostic accuracy and builds trust among healthcare professionals for future clinical use.