From Model Metrics to Real-World Healthcare Impact

A useful healthcare AI evaluation framework should move beyond:

Model performance → Workflow impact → Clinical impact → Patient outcomes

Technical metrics such as AUROC, sensitivity, specificity, and calibration remain important.

But deployment introduces additional questions.

Did decision-making improve?

Did workload decrease?

Did delays decrease?

Did patient outcomes improve?

Did performance remain equitable?

For agentic AI, task completion should also be separated from meaningful outcome improvement.

A system can automate thousands of actions without creating better healthcare.

Measure the outcome, not just the activity.

I am open to remote roles globally.