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Related Experiment Video

Updated: Aug 23, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring

Ruey-Kai Sheu1, Mayuresh Sunil Pardeshi2

  • 1Department of Computer Science, Tunghai University, No. 1727, Section 4, Taiwan Blvd, Xitun District, Taichung 407224, Taiwan.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

eXplainable AI (XAI) in medicine is crucial for understanding patient conditions and ethical AI. This survey details medical XAI methods, case studies, and proposes a novel human-in-the-loop approach with improved feedback systems.

Keywords:
XAI recommendation systemXAI scoring systemapproacheXplainable Artificial Intelligence (XAI)medical XAIsurvey

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Area of Science:

  • Medical Artificial Intelligence
  • Explainable AI (XAI)

Background:

  • The integration of Artificial Intelligence (AI) in healthcare necessitates explainability for legal and ethical compliance.
  • Understanding patient conditions and treatment decisions requires transparent AI models.

Purpose of the Study:

  • To present a comprehensive survey of eXplainable AI (XAI) in the medical domain.
  • To explore model enhancements, evaluation methods, case studies, and datasets relevant to medical XAI.
  • To propose advancements in XAI feedback systems and scoring mechanisms for healthcare applications.

Main Methods:

  • Review of existing AI and XAI methodologies, including local/global methods, knowledge distillation, and interpretable machine learning.
  • Analysis of practical case studies to demonstrate current XAI progress in medicine.
  • Development of a user-in-the-loop approach emphasizing human-machine collaboration.

Main Results:

  • Identification of key XAI characteristics and future trends in healthcare explainability.
  • Insights into prerequisite considerations for initiating medical XAI projects.
  • Introduction of a novel XAI recommendation and scoring system to address limitations in current evaluation methods.

Conclusions:

  • The survey highlights the critical need for explainable solutions in high-impact medical applications.
  • A human-in-the-loop approach with enhanced feedback mechanisms can improve the reliability of medical XAI.
  • Further development and implementation of XAI are essential for advancing ethical and effective AI in healthcare.