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Related Experiment Video

Updated: Dec 21, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping

Seyedeh Neelufar Payrovnaziri1, Zhaoyi Chen2, Pablo Rengifo-Moreno3,4

  • 1School of Information, Florida State University, Tallahassee, Florida, USA.

Journal of the American Medical Informatics Association : JAMIA
|May 18, 2020
PubMed
Summary

Explainable artificial intelligence (XAI) in medicine needs better evaluation and reproducibility. This review categorizes XAI methods in electronic health records, highlighting research gaps and future directions for advancing AI in healthcare.

Keywords:
Explainable artificial intelligence (XAI)deep learningelectronic health recordsinterpretable machine learningreal-world data

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Area of Science:

  • Artificial Intelligence in Medicine
  • Biomedical Informatics
  • Machine Learning in Healthcare

Background:

  • Explainable Artificial Intelligence (XAI) is crucial for understanding AI decisions in healthcare.
  • Electronic health records (EHRs) offer rich data for AI applications but require interpretable models.
  • Current XAI methods in EHRs lack standardized evaluation and reproducibility.

Purpose of the Study:

  • To systematically review XAI models utilizing real-world EHR data.
  • To categorize XAI techniques by biomedical application.
  • To identify research gaps and propose future directions for XAI in medicine.

Main Methods:

  • A systematic scoping review of literature from MEDLINE, IEEE Xplore, and ACM Digital Library (2009-2019).
  • Categorization of XAI methods including knowledge distillation, intrinsically interpretable models, dimensionality reduction, attention mechanisms, and feature importance.
  • Assessment of study reproducibility based on data and code availability.

Main Results:

  • Forty-two articles were included, revealing trends in XAI research and common diseases studied.
  • XAI methods were grouped into five main categories, with knowledge distillation and rule extraction being most prevalent.
  • Significant gaps were identified in the formal evaluation and reproducibility of XAI studies in medical applications.

Conclusions:

  • XAI evaluation in medical applications is underdeveloped and lacks formal practice.
  • Reproducibility of XAI research in healthcare remains a critical concern.
  • There are substantial opportunities to advance XAI research and application in medicine.