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Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator
Nafiul Nipu1, L V van Dijk2, Guadalupe Canahuate3
1University of Illinois Chicago.
Summary
We developed an AI-powered dashboard for analyzing head and neck cancer patient outcomes. This tool helps clinicians understand individual patient prognoses using cohort data and visual analytics.
Area of Science:
- Oncology
- Artificial Intelligence
- Data Visualization
- Precision Medicine
Background:
- Precision medicine utilizes ensemble patient datasets for outcome prediction.
- A significant challenge lies in deploying understandable AI predictive models using multi-institutional data.
- Head and neck cancer treatment requires accurate patient outcome estimation.
Purpose of the Study:
- To describe lessons learned from developing and deploying an interactive dashboard for analyzing individual head and neck cancer patient outcomes.
- To create an AI solution with a multi-view interface for visual analysis and risk stratification.
- To evaluate the dashboard's usability and impact with clinician domain experts.
Main Methods:
- Development of an interactive dashboard integrating AI predictive models.
- Implementation of a multi-view interface with domain-specific plots for visual analysis.
- Public deployment of the dashboard and subsequent evaluation by clinician experts.
Main Results:
- The dashboard successfully integrates AI with visual analytics for patient outcome analysis.
- It enables quick stratification of new patients into risk groups.
- Post-deployment evaluation provided valuable clinician feedback and insights.
Conclusions:
- The developed dashboard effectively supports the analysis of individual head and neck cancer patient outcomes.
- Lessons learned from development and deployment offer insights for similar AI tool implementations.
- Interactive dashboards can bridge the gap between complex AI models and clinical decision-making.
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