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Explainable AI in nuclear medicine.

Sune Holm1, Daria Ferrara2, Miriam Pepponi3

  • 1Department of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark. suneh@ifro.ku.dk.

European Journal of Nuclear Medicine and Molecular Imaging
|November 25, 2025
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Summary
This summary is machine-generated.

Explainable artificial intelligence (AI) is crucial for tasks that clinicians cannot independently verify. For AI predicting cancer cachexia, explanations are needed when outputs are uncertain or errors occur, ensuring trustworthy clinical tools.

Keywords:
CachexiaClinical decision-makingExplainable AILung cancerMedical imagingTrustworthy AI

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Cachexia significantly impacts cancer patient outcomes.
  • Predictive models for cachexia are increasingly complex.
  • The need for transparency in AI decision-making is growing.

Purpose of the Study:

  • To explore the necessity of explainable AI (XAI) in a multi-disciplinary project focused on predicting cancer cachexia.
  • To define criteria for when AI explainability is essential in clinical settings.

Main Methods:

  • Multi-disciplinary project meetings involving medicine, data science, sociology, and philosophy.
  • Discussion and analysis of AI explainability requirements in different clinical contexts.

Main Results:

  • Distinguished between AI tasks users can perform/validate versus those they cannot.
  • Identified scenarios where AI explainability is indispensable.

Conclusions:

  • AI explanations are vital for tasks beyond user validation capabilities.
  • Documented reliability may suffice for verifiable outputs, but explainability enhances trust, especially with uncertainty or errors.
  • Close collaboration among clinicians, AI developers, and stakeholders is key for trustworthy AI in practice.