Context

MediScan, a healthcare startup, uses a binary classification model to flag chest X-rays for possible pneumonia so radiologists can prioritize urgent cases. The team is debating whether the current model is acceptable because leadership sees high precision, while clinicians are worried about missed positive cases.

Current Performance

Confusion Matrix

The Problem

The VP of Product argues that 91% precision means the model is “very accurate” and should be deployed broadly. The head radiologist argues that recall of 68% is too low because missed pneumonia cases can delay treatment. You need to explain the difference between precision and recall using the model's actual results and recommend which metric should matter more in this use case.

Requirements

1. Define precision and recall clearly using the numbers above.
2. Explain what each metric says about model behavior in this medical setting.
3. Compare the business and clinical impact of false positives vs false negatives.
4. State whether the current threshold appears too conservative or too aggressive.
5. Recommend how you would adjust or evaluate the model next.

Constraints

• Radiologists can review at most 700 flagged scans per day.
• Missing a true pneumonia case is considered more costly than reviewing a false alarm.
• Any recommendation must preserve clinician trust and workflow efficiency.