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Updated: Jul 4, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Explainable Artificial Intelligence for Clinical Text Classification: A Scoping Review of Methods and Applications.

Haifa Sridi1, Akram Redjdal2, Brigitte Seroussi1,3

  • 1Sorbonne Université, INSERM, Université Sorbonne Paris Nord, LIMICS, Paris, France.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
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This summary is machine-generated.

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Explainable artificial intelligence (XAI) methods improve transparency in clinical text classification using electronic health records. Transformer models show high performance, with attention mechanisms and SHAP/LIME offering key interpretability insights.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing for Clinical Data

Background:

  • Machine learning (AI) systems in healthcare often function as "black boxes," limiting interpretability and clinical trust.
  • Transparency is crucial for adopting AI in clinical settings, especially when analyzing unstructured electronic health record (EHR) data.
  • Clinical text classification tasks require interpretable AI models to ensure reliable decision-making.

Purpose of the Study:

  • To conduct a scoping review of explainable artificial intelligence (XAI) approaches applied to clinical text classification.
  • To analyze the methods used for interpreting AI models processing unstructured EHR data.
  • To identify trends and performance of XAI techniques in healthcare AI applications.

Main Methods:

Keywords:
Clinical Text ClassificationExplainable Artificial IntelligenceLIMESHAP

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Published on: June 13, 2025

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Last Updated: Jul 4, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

  • Systematic literature search and analysis of 22 studies focusing on XAI in clinical text classification.
  • Categorization of studies based on classification tasks (binary, multi-class, multi-label) and AI model architectures.
  • Review of both model-specific (e.g., attention mechanisms) and model-agnostic (e.g., SHAP, LIME) XAI techniques.

Main Results:

  • Transformer-based models demonstrated superior performance in clinical text classification tasks.
  • Attention mechanisms were frequently employed as model-specific XAI methods within deep learning models.
  • Model-agnostic methods like SHAP and LIME offered versatile interpretability across various AI architectures.

Conclusions:

  • XAI methods significantly enhance the transparency and trustworthiness of AI systems used in clinical text analysis.
  • The study highlights the effectiveness of both specialized and general XAI approaches for EHR data.
  • Further research is necessary to validate the clinical utility and reliability of AI explanations in healthcare.