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Clinician-informed XAI evaluation checklist with metrics (CLIX-M) for AI-powered clinical decision support systems
Aida Brankovic1,2, David Cook3, Jessica Rahman4
1CSIRO's Australian eHealth Research Centre, Herston, QLD, Australia. aida.brankovic@csiro.au.
NPJ Digital Medicine
|June 14, 2025
Summary
Clinical explainable AI (XAI) lacks standardized evaluation metrics. We created a clinician-informed checklist to guide XAI reporting, aiming to improve trust and adoption in healthcare.
Area of Science:
- Artificial Intelligence in Medicine
- Clinical Informatics
- Health Technology Assessment
Background:
- Rapid proliferation of clinical explainable AI (XAI) models.
- Concerns regarding unclear purposes and unrealistic expectations for AI explanations.
- Absence of standardized metrics for evaluating XAI effectiveness.
Purpose of the Study:
- To address the lack of standardized evaluation metrics for clinical XAI.
- To propose a framework for transparent reporting of XAI methods.
- To enhance trust, reduce risks, and foster AI adoption in clinical settings.
Main Methods:
- Development of a 14-item checklist.
- Informed by clinical end-users (clinicians).
- Incorporated clinical, machine learning, and decision-making attributes.
Main Results:
- The first clinician-informed checklist for XAI evaluation.
- A foundational tool for standardizing XAI reporting.
- A step towards transparent and reliable AI in healthcare.
Conclusions:
- The developed checklist is the initial step towards standardizing clinical XAI.
- Transparent reporting of XAI methods is crucial for trust and adoption.
- This work aims to determine the true clinical potential of applied XAI.
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