Rationale
Worklist prioritization AI flags studies with high probability of cancer to accelerate radiologist review and reduce time to diagnosis. Faster review can shorten the interval between screening and diagnostic workup for positive cases. Prioritization must balance sensitivity with manageable false positive rates.
Performance Metrics
Evaluate triage models on time to read positive cases and impact on recall and cancer detection rates. Monitor false positive burden and radiologist workload to avoid alert fatigue. Continuous monitoring ensures stable performance across populations.
Workflow Integration
Integrate prioritization outputs into the reading worklist with clear indicators and override options for radiologists. Define escalation pathways for urgent findings and measure downstream effects on biopsy scheduling. Provide training on interpreting AI prioritization and limitations.
Equity and Access
Validate models across diverse populations to avoid disparities in prioritization performance. Monitor outcomes by demographic groups and adjust thresholds or retrain models as needed. Ensure transparent communication with patients and clinicians about AI assisted workflows.