Context

MediScan uses a binary classifier to flag chest X-rays for possible pneumonia so radiologists can prioritize urgent cases. The team is concerned that the current model appears highly accurate when it predicts a positive case, but it may be missing too many actual pneumonia patients.

Current Performance

On a recent validation set of 10,000 X-rays, the model produced the following counts:

The Problem

The radiology lead wants to understand what it means, in this context, for the classifier to have high precision but low recall. The team must decide whether the model is too conservative and whether threshold or workflow changes are needed.

Requirements

1. Explain what high precision and low recall mean using the numbers above.
2. Describe the business and clinical implications of false positives vs. false negatives.
3. Interpret whether the current threshold is likely too high or appropriate.
4. Recommend specific changes to improve model usefulness without overwhelming radiologists.

Constraints

• Radiologists can review only 500 flagged scans per day.
• Missing a true pneumonia case is considered much more costly than reviewing a false alarm.
• Any proposed change must preserve trust in the triage system and fit into the current workflow.