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Clinician's Artificial Intelligence Checklist and Evaluation Questionnaire: Tools for Oncologists to Assess
Nadia S Siddiqui1, Yazan Bouchi2, Syed Jawad Hussain Shah3
1University of Washington School of Medicine, Seattle, WA.
JCO Clinical Cancer Informatics
|September 17, 2025
Summary
This study introduces practical tools for oncologists to evaluate artificial intelligence (AI) models in cancer care. These checklists and questionnaires aid clinicians in assessing AI applications for better integration into oncology.
Area of Science:
- Oncology
- Artificial Intelligence
- Machine Learning
Background:
- Oncology is rapidly advancing with AI and machine learning, necessitating careful evaluation of AI models.
- Existing AI evaluation guidelines are developer-focused and lack clinical applicability or specificity for oncology.
- Clinicians need user-friendly tools to assess AI tools in their practice.
Purpose of the Study:
- To provide oncologists with practical tools for evaluating AI models.
- To develop a yes/no checklist for efficient AI model screening.
- To create an open-ended questionnaire for in-depth AI model assessment.
Main Methods:
- Developed a yes/no checklist and an open-ended questionnaire for AI model evaluation.
- Incorporated insights from clinical and AI researchers.
- Conducted two literature searches and integrated findings from 24 articles.
- Analyzed four AI applications in oncology.
Main Results:
- Created a yes/no checklist for rapid AI model evaluation against best standards.
- Developed an in-depth questionnaire for comprehensive AI model assessment.
- Demonstrated the utility of evaluation tools through case studies of AI in oncology.
Conclusions:
- The developed tools facilitate effective AI model assessment by oncologists.
- These resources support the interdisciplinary integration of AI in oncology.
- Case analyses enhance clinical understanding of AI applications in cancer care.
