Pre-Deployment Validation of Radiology AI: How CARPL Makes it Simple

Radiology AI has great promise, but published studies and regulatory approvals like FDA and CE don’t always reflect real-world performance. Algorithms that work well in one hospital may fail in another due to different scanners, protocols, and varying patient demographics.

Most pre-deployment validations are done for singular AI applications, making it hard to judge relative value. Every AI solution usually requires new infrastructure, security reviews, performance evaluation template, and custom integrations while providing different outputs and metrics, which makes comparison nearly impossible.

CARPL provides a universal validation workflow to run and compare different AI and even compare in-house models to commercially available AI applications. Load your datasets, integrate into a safe environment, and compare results fairly.

CARPL’s Validation Workflow

1. Choose Al
Select one or more AI applications for the intended use case.

2. Create a Dataset
Upload DICOM files from the browser with built-in HIPAA-compliant de-identification, or add them via API or DICOM push.

3. Add Reports
Upload ground truth in tabular format or as free text reports; extract findings automatically from reports using LLMs within the platform.

4. Run Validation Project
Create a testing and monitoring project where AI predictions are compared with radiologists’ opinions. Leave feedback using custom templates. Evaluate AI performance for each study, compare outputs visually and statistically, and view aggregate measures such as sensitivity, specificity, PPV, etc.

4. Bias & Fairness Checks
Analyze performance across subgroups such as sex, age, BMI, scanner type using integrated bias and fairness frameworks like Aequitas.

5. Reporting
Generate one-click structured reports for summary statistics and bias and fairness estimation, ready for internal review and sharing.

Key Metrics We Track

CARPL goes beyond a single accuracy score, providing a complete picture:

• Sensitivity (Recall): How well positives are detected; crucial for safety.
  
  

• Specificity: Ability to rule out negatives and avoid alert fatigue.
  
  

• Positive Predictive Value (PPV): Of flagged cases, how many are true positives.
  
  

• F1-Score: Balance of sensitivity and PPV, useful in imbalanced datasets.
  
  

• AUC (ROC): Threshold-independent measure of overall discriminative ability.
  
  

• Error Review: Examine false positives and false negatives at the case level.
  
  

• Bias Metrics: Parity of performance across demographic or equipment groups.
  
  

• Monitoring Metrics: Tools like JSD (stability) and SM1 (predictive divergence) track performance drift after deployment.
  
  

Why CARPL Is Different

• Single UI for comparison: no switching between viewers or formats. Single user interface to view all AI results.
  
  

• Comparative validation: test multiple AIs or ensemble them for head-to-head comparison, with visibility into comparative study-level AI probabilities.
  
  

• Regulator-ready outputs: automated, structured validation reporting.
  
  

• Continuous Monitoring: monitor AI performance after live production without any gaps.

Validation ensures safety and reliability, and CARPL ensures validation itself is unified, fast, and scalable. With our validation and testing module, CARPL empowers healthcare providers to test multiple AIs to find the right fit for their practice.