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Related Experiment Video

Updated: Jul 15, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Published on: August 16, 2020

Examining explainable artificial intelligence in TNM staging with PET-CT: a user-centred observation study.

Charlie Baskerville1,2, Julien M Y Willaime3, Vineet Prakash4,5,6

  • 1Centre for Vision, Speech and Signal Processing, The University of Surrey, Guildford, UK. c.baskerville@surrey.ac.uk.

La Radiologia Medica
|July 13, 2026
PubMed
Summary

Explainable AI (XAI) significantly boosts radiologists' willingness to adopt artificial intelligence clinical decision support systems (AI-CDSS) in nuclear medicine. XAI provides useful insights for confirming or challenging AI recommendations, aiding trust and integration.

Keywords:
AIClinical decision support systemsImplementation sciencePET-CTRadiologyXAI

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Radiology and Nuclear Medicine

Background:

  • Artificial intelligence (AI) offers potential efficiency gains in radiology and nuclear medicine, addressing workforce shortages.
  • Slow adoption of AI-based clinical decision support systems (AI-CDSS) is attributed to limited model transparency.
  • Explainable AI (XAI) aims to enhance clinician trust and acceptance by providing model transparency.

Purpose of the Study:

  • To evaluate the impact of different XAI explanation types on radiologists' willingness to adopt AI systems.
  • To assess the usefulness of XAI explanations in clinical decision-making for nuclear medicine.

Main Methods:

  • Ten nuclear medicine radiologists used a simulated AI-CDSS for lung cancer TNM staging on PET/CT scans.
  • Three XAI approaches were tested: input feature attribution, concept explanations, and global transparency.
  • Radiologists rated adoption likelihood and explanation usefulness; qualitative interviews were also conducted.

Main Results:

  • All XAI methods significantly increased adoption willingness compared to a black-box AI model (p < 0.05).
  • Explanations were consistently useful for confirming or challenging AI staging recommendations (p < 0.001).
  • Key factors influencing preferences included clinical relevance, error detection, decision support value, and a balance between explanation depth and usability.

Conclusions:

  • XAI integration into nuclear medicine CDSS enhances radiologist acceptance and provides clinically relevant oversight information.
  • These findings support XAI's role in facilitating the integration of AI tools into diagnostic workflows, aligning with regulatory expectations like the EU AI Act.