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Radiology AI Lab: Evaluation of Radiology Applications with Clinical End-Users.

Olivier Paalvast1, Merlijn Sevenster2,3, Omar Hertgers2

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Summary
This summary is machine-generated.

A new Radiology AI lab objectively measures artificial intelligence (AI) impact on radiologist workflows using biometrics. AI-assisted interpretation showed more efficient visual search strategies, indicating improved radiologist performance.

Keywords:
Eye-trackingObjective real-time sensingRadiology AI labUser-state sensing

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Human-Computer Interaction

Background:

  • Widespread adoption of AI in radiology is limited despite numerous EU approvals.
  • Current AI assessments lack real-time radiologist-AI interaction data.
  • Objective measurement of AI's impact on clinical workflows is needed.

Purpose of the Study:

  • To establish a Radiology AI lab for real-time, objective measurement of AI's impact on radiologist workflows.
  • To implement a user-state sensing framework (USSF) for analyzing radiologist-AI interactions.
  • To utilize non-invasive biometric techniques for workflow analysis.

Main Methods:

  • Developed the user-state sensing framework (USSF) categorizing interactions into personal, interactional, and contextual states.
  • Established a Radiology AI lab using eye-tracking, heart rate monitoring, and facial expression analysis.
  • Conducted a pilot study with radiologists interpreting ultra-low-dose CT cases in standard vs. AI-annotated PACS.

Main Results:

  • The Radiology AI lab was successfully realized, capturing biometric data.
  • No significant difference in interpretation time was observed between standard and AI-annotated PACS.
  • Three of four radiologists exhibited significant shifts in eye-tracking metrics (e.g., saccade duration, fixation duration, pupil diameter) in the AI-annotated workflow.
  • Heart rate variability and facial expression analysis identified key moments during interpretation.

Conclusions:

  • The Radiology AI lab effectively enables objective analysis of radiologist workflows and AI tool impact.
  • Observed changes in eye-tracking metrics suggest AI-assisted interpretation may lead to more efficient visual search strategies.
  • Future work will expand contextual state sensing and investigate more AI-enabled tools.