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Related Experiment Video

Updated: Apr 24, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Integrating Confidence, Difficulty, and Language Model Calibration for Better Explainability in Clinical Documents

Mihai Horia Popescu1, Kevin Roitero1, Vincenzo Della Mea1

  • 1Department of Mathematics, Computer Science and Physics (DMIF), University of Udine, via Delle Scienze, 206, Udine, 33100, Italy, 39 0432 558400.

JMIR AI
|April 22, 2026
PubMed
Summary
This summary is machine-generated.

This study enhances the interpretability of deep learning models for clinical document annotation. By using calibrated confidence and saliency maps, we improve trust in AI for medical applications.

Keywords:
cause of death predictiondeep learninginstance difficultymachine learningmodel confidencepredictionsaliency mapssemantic

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Clinical Informatics

Background:

  • Increasing interest in deep learning for clinical document annotation.
  • Challenges in interpretability and transparency of complex models.

Purpose of the Study:

  • Improve interpretability of transformer models for clinical document annotation.
  • Evaluate explainability of deep learning models using coded clinical data from death certificates.
  • Leverage calibrated confidence, saliency maps, and instance difficulty for model interpretation.

Main Methods:

  • Utilized disease language bidirectional encoder representations from transformers (DiBERT) pretrained on International Statistical Classification of Diseases and Related Health Problems (ICD) data.
  • Analyzed death certificate data (2014-2017) with textualized ICD codes.
  • Applied temperature scaling for model calibration and Variance of Gradients for instance difficulty analysis.
  • Employed saliency maps (Integrated Gradients) for word-level attribution.

Main Results:

  • Achieved high accuracy (0.990) in predicting the cause of death.
  • Demonstrated good initial model calibration (Expected Calibration Error: 1.40).
  • Showcased effectiveness of Variance of Gradients in analyzing model behavior and identifying out-of-distribution cases.

Conclusions:

  • Enhanced interpretability and explainability improve the practical utility of deep learning in clinical document annotation.
  • Proposed approaches support trustworthy application of AI in critical medical settings.
  • Highlighted the need to address data limitations for robust performance, especially in complex cases.