AI is changing radiology.
It’s driving earlier detection, easing workloads, and boosting diagnostic accuracy.
But concerns remain:
Without a clear, standardized way to measure AI performance, adoption stalls, and trust wavers.
Bialogics AI (AIQ) Framework continuously monitors and validates AI models, giving you real-time assurance and actionable insights, not just performance promises.
For greater quality assurance, DImax analyses and monitors for ACR critical findings codes to monitor findings and radiologist call log reports
The agreement or disagreement between AI findings and radiologist-reported diagnoses.
Sensitivity is the AI’s ability to correctly identify positive cases while Specificity is AI’s ability to correctly classify negative cases.
PPV is the proportion of AI detected cases that area acuratetely determined as positives NPV are the proportion of AI detected cases that are correctively identified as negative.
Drift Reporting and Trends track the Accuracy and FI scoring over time of each algorithm in use to identify long- term tending bias or tolerance change.
Augmented Diagnostic Rate (ADR) refers to the relative or absolute increase in correctly identified diagnoses due to the implementation of an AI system compared to standard diagnostic practice.
Prevalence refers to the proportion of imaging studies that truly contain a specific pathology within the dataset used to evaluate AI diagnostic performance.
The AIQ Score in AI monitoring is a composite metric that quantifies the intelligence level of an AI system by benchmarking its diagnostic accuracy, consistency, and adaptability against expert radiologist performance.