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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 manifesto on explainability for artificial intelligence in medicine.

Carlo Combi1, Beatrice Amico1, Riccardo Bellazzi2

  • 1University of Verona, Verona, Italy.

Artificial Intelligence in Medicine
|November 3, 2022
PubMed
Summary

Artificial intelligence (AI) explainability is crucial in biomedicine for patient safety. This paper defines explainable AI (XAI) and outlines key requirements for its use in healthcare.

Keywords:
Artificial intelligenceExplainabilityExplainable artificial intelligenceInterpretabilityInterpretable artificial intelligence

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Area of Science:

  • Biomedical informatics
  • Artificial intelligence
  • Computer science

Background:

  • Growing use of artificial intelligence (AI) in various applications.
  • Concerns regarding the "black box" nature of AI and its lack of transparency.
  • Heightened importance of AI explainability in biomedical fields due to patient safety implications.

Purpose of the Study:

  • To explore the concept of explainable AI (XAI) in depth.
  • To provide a functional definition and conceptual framework for XAI.
  • To identify desiderata for achieving AI explainability in biomedicine.

Main Methods:

  • A position paper synthesizing perspectives from seven researchers with diverse roles in AI and biomedicine.
  • Conceptual analysis of explainable AI (XAI).
  • Identification of key domains within biomedicine relevant to AI explainability.

Main Results:

  • A functional definition and conceptual framework for XAI are proposed.
  • A series of desiderata for achieving explainability in AI within biomedical contexts are presented.
  • The paper highlights the critical need for understandable AI outputs in healthcare.

Conclusions:

  • Explainable AI (XAI) is essential for safe and effective AI implementation in biomedicine.
  • The proposed framework and desiderata offer guidance for developing and deploying explainable AI systems.
  • Addressing AI explainability is vital for user trust and adoption in medical applications.