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

Yupei Li1, Qiyang Sun1, Alican Akman1

  • 1GLAM - Group on Language, Audio, & Music, Imperial College London, UK.

Studies in Health Technology and Informatics
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

Explainable AI (XAI) is crucial for trustworthy healthcare applications. XAI enhances transparency in artificial intelligence (AI) models, improving clinical decisions and patient trust.

Keywords:
AI applicationArtificial IntelligenceDigital HealthExplainable AIHealthcare

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Last Updated: Feb 24, 2026

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

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Healthcare Technology

Background:

  • Artificial intelligence (AI) integration in healthcare is rapidly advancing, impacting diagnosis, treatment, and patient care.
  • Complex AI models often lack transparency, posing risks in high-stakes medical decision-making.
  • Explainability is vital for informed clinical decisions and patient trust in AI.

Purpose of the Study:

  • To introduce foundational concepts of explainable AI (XAI).
  • To analyze XAI methodologies for enhancing AI model interpretability in healthcare.
  • To discuss XAI applications across diverse healthcare data modalities.

Main Methods:

  • Review of foundational explainable AI (XAI) concepts.
  • Analysis of prominent methodologies for enhancing AI model interpretability.
  • Examination of XAI applications across time-series, text, image, and audio medical data.

Main Results:

  • XAI provides transparency essential for healthcare professionals and patients.
  • XAI addresses unique challenges across various medical data types.
  • Identified prominent XAI methodologies applicable to healthcare domains.

Conclusions:

  • XAI is critical for safe and effective AI implementation in healthcare.
  • Current limitations and challenges in healthcare XAI implementation were discussed.
  • Promising future research directions for enhancing AI reliability in healthcare were identified.